Probability and statistics Books
Springer Verlag, Singapore Machine Learning Methods
Book SynopsisThis book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning. Table of ContentsChapter 1 Introduction to Machine learning and Supervised Learning.- Chapter 2 Perceptron.- Chapter 3 K-Nearest-Neighbor.- Chapter 4 The Naïve Bayes Method.- Chapter 5 Decision Tree.- Chapter 6 Logistic Regression and Maximum Entropy Model.- Chapter 7 Support Vector Machine.- Chapter 8 Boosting.- Chapter 9 EM Algorithm and Its Extensions.- Chapter 10 Hidden Markov Model.- Chapter 11 Conditional Random Field.
£75.99
Quercus Publishing How to Expect the Unexpected: The Science of
Book SynopsisA Waterstones Best Popular Science Book of 2023'Delightfully clear and vivid to read...A splendid book! Philip Pullman'Absolutely fascinating' James O'Brien'An exceptional book - readable, funny and more needed than ever' Dr Chris van Tulleken, bestselling author of Ultra-Processed PeopleAre you more likely to become a professional footballer if your surname is Ball?· How can you be one hundred per cent sure you will win a bet?· Why did so many Pompeiians stay put while Mount Vesuvius was erupting?· How do you prevent a nuclear war?Ever since the dawn of human civilisation, we have been trying to make predictions about what's in store for us. We do this on a personal level, so that we can get on with our lives efficiently (should I hang my laundry out to dry, or will it rain?). But we also have to predict on a much larger scale, often for the good of our broader society (how can we spot economic downturns or prevent terrorist attacks?). For just as long, we have been getting it wrong. From religious oracles to weather forecasters, and from politicians to economists, we are subjected to poor predictions all the time. Our job is to separate the good from the bad. Unfortunately, the foibles of our own biology - the biases that ultimately make us human - can let us down when it comes to making rational inferences about the world around us. And that can have disastrous consequences.How to Expect the Unexpected will teach you how and why predictions go wrong, help you to spot phony forecasts and give you a better chance of getting your own predictions correct.Trade ReviewA vivid, wide-ranging and delightful guide to the light and the dark side of prediction * Tim Harford, bestselling author of How to Make the World Add Up *Kit Yates presents maths as it should be taught to everyone: accessible, fun, stimulating, and deeply relevant to our lives. Spend some time with this book and you're likely to make better judgements and decisions, to see through the charlatans and snake-oil salespeople - and perhaps even to fool yourself a little less. * Philip Ball, author of the award-winning Critical Mass *Fascinating and fun. From the everyday to global challenges, Kit Yates explores how changing your mind - so often thought to be a weakness - is the best life skill we can all acquire. A brilliant book * Professor Alice Roberts *Yates' writing is a beacon of clarity sorely needed in a complicated and confusing world. How do we overcome our biases, understand coincidences or tackle the unreliability of our intuition? With bountiful familiar examples, he effortlessly overturns so many of our deep-rooted wrong-headed notions gently and persuasively. I'll be quoting from this book * Jim Al-Khalili *I'm a Yates fan. His style is all-clarity-no-bullshit * Aperiodical *Seriously good * Caroline Lucas MP *Absolutely fascinating * James O'Brien *An exceptional book - readable, funny and more needed than ever * Dr Chris van Tulleken, bestselling author of Ultra-Processed People *Yates' writing style imbues the subjects covered with an infectious enthusiasm, artfully dispelling the dry, stuffy perceptions many people have of maths * Physics World *HOW TO EXPECT THE UNEXPECTED is fascinating and (very much to the point) delightfully clear and vivid to read. Like many people, I like reading about maths without actually knowing how to do it, and part of the pleasure of reading this came from its many examples from everyday life. A splendid book! * Philip Pullman *
£18.75
MIT Press Fundamentals of Probability and Statistics for Machine Learning
a huge range and FREE tracked UK delivery on ALL orders.
£76.50
Basic Books The Model Thinker: What You Need to Know to Make
Book SynopsisFrom the stock market to covid-19, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models-from linear regression to random walks and far beyond-that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. Now culminating in an examination of how to use the multi-model approach to think about pandemics like covid-19, The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.
£15.29
Harvard University Press Game Theory and the Law
Book SynopsisThis book promises to be the definitive guide to the field. It provides a highly sophisticated yet exceptionally clear explanation of game theory, with a host of applications to legal issues.Trade ReviewGame Theory and the Law promises to be the definitive guide to the field. It provides a highly sophisticated yet exceptionally clear explanation of game theory, with a host of applications to legal issues. The authors have not only synthesized the existing scholarship, but also created the foundation for the next generation of research in law and economics. -- Daniel A. Farber, University of Minnesota Law SchoolThe most comprehensive and encompassing treatment of this approach… [This] is the first nontechnical, modern introduction to how (noncooperative) game theory can be applied specifically to legal analysis… Game Theory and the Law is a user-friendly analysis of concrete, numerical examples, rather than a theoretical presentation of abstract concepts. The authors introduce and explain, with actual legal cases or hypotheticals, the salient issues of modern game theory. This breadth of coverage is remarkable. This is not just a textbook; it is also something of a research monograph, introducing many new models attributable to the authors alone. -- Peter H. Huang * Jurimetrics Journal *Game Theory and the Law is an important book. It is important in the sense that it will serve as a catalyst for an expanded use of game-theoretic models in the study of law. It will be a book that people will one day recognize as having had a considerable influence on its field. And it will receive the praise that accompanies such influence. Happily, such influence will be beneficial to the field of law and such praise will be richly deserved, because Game Theory and the Law is an extremely intelligent and thoughtful text… One of the features of the book that is most striking (and, for my part, most welcome) is the thoughtful and sensible manner in which they approach the use of game theory. Unlike many proponents of game-theoretic analysis, they do not present it as the only legitimate approach to social-scientific analysis. The authors present game theory as a powerful tool that can be used along with other approaches to enhance our understanding of the role of law in social life… The persuasiveness of their general argument for the utility of game theory derives from a combination of the power of their insights along with the sensibility of their analysis. The book is written in a clear, concise and interesting manner. Its bibliographic references render it a source book for additional research in both game theory and law. This is a book that should be read by scholars of law in particular and scholars of political behavior in particular. -- Jack Knight * Law and Politics Book Review *Table of ContentsPreface Introduction: Understanding Strategic Behavior Bibliographic Notes Simultaneous Decisionmaking and the Normal Form Game The Normal Form Game Using Different Games to Compare Legal Regimes The Nash Equilibrium Civil Liability, Accident Law, and Strategic Behavior Legal Rules and the Idea of Strict Dominance Collective Action Problems and the Two-by-Two Game The Problem of Multiple Nash Equilibria Summary Bibliographic Notes Dynamic Interaction and the Extensive Form Game The Extensive Form Game and Backwards Induction A Dynamic Model of Preemption and Strategic Commitment Subgame Perfection Summary Bibliographic Notes Information Revelation, Disclosure Laws, and Renegotiation Incorporating Beliefs into the Solution Concept The Perfect Bayesian Equilibrium Solution Concept Verifiable Information, Voluntary Disclosure, and the Unraveling Result Disclosure Laws and the Limits of Unraveling Observable Information, Norms, and the Problem of Renegotiation Optimal Incentives and the Need for Renegotiation Limiting the Ability of Parties to Renegotiate Summary Bibliographic Notes Signaling, Screening, and Nonverifiable Information Signaling and Screening Modeling Nonverifiable Information Signals and the Effects of Legal Rules Information Revelation and Contract Default Rules Screening and the Role of Legal Rules Summary Bibliographic Notes Reputation and Repeated Games Backwards Induction and Its Limits Infinitely Repeated Games, Tacit Collusion, and Folk Theorems Reputation, Predation, and Cooperation Summary Bibliographic Notes Collective Action, Embedded Games, and the Limits of Simple Models Collective Action and the Role of Law Embedded Games Understanding the Structure of Large Games Collective Action and Private Information Collective Action Problems in Sequential Decisionmaking Herd Behavior Summary Bibliographic Notes Noncooperative Bargaining Modeling the Division of Gains from Trade Legal Rules as Exit Options Bargaining and Corporate Reorganizations Collective Bargaining and Exit Options Summary Bibliographic Notes Bargaining and Information Basic Models of the Litigation Process Modeling Separate Trials for Liability and Damages Information and Selection Bias Discovery Rules and Verifiable Information Summary Bibliographic Notes Conclusion: Information and the Limits of Law Notes References Glossary Index
£34.81
O'Reilly Media Learning Data Science
Book SynopsisLearning Data Science is the first book to cover foundational skills in both programming and statistics that encompass the entire data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.
£53.99
Macmillan Learning Research Methods
Book Synopsis
£63.64
HarperCollins Publishers Thinking Better The Art of the Shortcut
Book SynopsisHow do you remember more and forget less?How can you earn more and become more creative just by moving house?And how do you pack a car boot most efficiently?This is your shortcut to the art of the shortcut.Mathematics is full of better ways of thinking, and with over 2,000 years of knowledge to draw on, Oxford mathematician Marcus du Sautoy interrogates his passion for shortcuts in this fresh and fascinating guide. After all, shortcuts have enabled so much of human progress, whether in constructing the first cities around the Euphrates 5,000 years ago, using calculus to determine the scale of the universe or in writing today's algorithms that help us find a new life partner.As well as looking at the most useful shortcuts in history such as measuring the circumference of the earth in 240 BC to diagrams that illustrate how modern GPS works Marcus also looks at how you can use shortcuts in investing or how to learn a musical instrument to memory techniques. He talks to, among many, the Trade Review‘enjoyably clever …with vividly illustrated chapters about the real-world applications of algebra, geometry, probability theory…It’s Du Sautoy, in the end, who provides the wisest commentary’ Steven Poole, Guardian ‘If you thought Maths was all about long stuff, like long division and long multiplication and taking a long, long time to figure things out, Marcus du Sautoy shows that it's just the opposite. Full of humour, stories and the lightest of touches, this is a sight-seeing tour of some of the world's greatest neat dodges, unexpected turns and useful cut-throughs. Prepare to be caught short’ Michael Rosen ‘This book will change the way you look at the world. It's chock full of stories, ideas and clever tricks – I loved it. Marcus is a maestro at making big ideas come alive – he deserves his place alongside Richard Dawkins, E. O. Wilson and Carlo Rovelli in the pantheon of great modern science writers’ Rohan Silva, CEO and founder of Second Home ‘If mathematics has proved anything, it is that shortcuts can change the world. Marcus du Sautoy has come up with a smart, well written and entertaining guide to the connecting tunnels, underpasses and other tricks to traverse the trials of everyday life’ Roger Highfield, author, broadcaster and Science Director at the Science Museum ‘The joy of du Sautoy’s book isn’t really the art of the real-world shortcut at all. It is the romp through mathematical ideas, from place value to non Euclidean geometry to probability theory…There are vivid historical examples of scientists and others using mathematical ideas to solve problems’ Tim Harford, Financial Times
£9.49
Cambridge University Press Computer Age Statistical Inference Student
Book SynopsisThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. ''Data science'' and ''machine learning'' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.Table of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First-Century Topics: 15. Large-scale hypothesis testing and false-discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Author Index; Subject Index.
£30.99
John Wiley & Sons Inc Statistical Models and Methods for Lifetime Data
Book SynopsisPraise for the First Edition An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . . -Choice This is an important book, which will appeal to statisticians working on survival analysis problems. -Biometrics A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook. -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, SecoTrade Review“...a welcome addition to the literature on survival analysis...for a unified and thorough reference of classical theory and models, this book is an excellent choice.” (Journal of the American Statistical Association, March 2004) "This book is a role-model for other who are planning to write books…every statistician and applied researcher ought to have this book in their collection." (Journal of Statistical Computation and Simulation, October 2003) "...expanded and updated with recent research...a valuable reference...this book...merits a place on the bookshelf of anyone concerned with the analysis of lifetime data from any field. (Technometrics, Vol. 45, No. 3, August 2003) "...updated version of the popular text...this excellent book will serve as either a reference or a graduate-level textbook." (Short Book Reviews, Vol. 23, No. 2, August 2003) "...excellent...provides a wealth of information for those familiar with the area." (Pharmaceutical Research, Vol. 20, No. 9, September 2003) "...the author's aim is to cover lifetime data analysis without concentrating exclusively on any field of applications...he succeeds quite well..." (Zentralblatt Math, 2003) “...rewritten to reflect new developments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 2, June 2003) "Compared with the large number of other good textbooks in the this field, this is one of the best. I highly recommend that all applied statisticians add this volume to their libraries." (Applied Clinical Trials, May 2003)Table of ContentsBasic Concepts and Models. Observation Schemes, Censoring and Likelihood. Some Nonparametric and Graphical Procedures. Inference Procedures for Parametric Models. Inference procedures for Log-Location-Scale Distributions. Parametric Regression Models. Semiparametric Multiplicative Hazards Regression Models. Rank-Type and Other Semiparametric Procedures for Log-Location-Scale Models. Multiple Modes of Failure. Goodness of Fit Tests. Beyond Univariate Survival Analysis. Appendix A. Glossary of Notation and Abbreviations. Appendix B. Asymptotic Variance Formulas, Gamma Functions and Order Statistics. Appendix C. Large Sample Theory for Likelihood and Estimating Function Methods. Appendix D. Computational Methods and Simulation. Appendix E. Inference in Location-Scale Parameter Models. Appendix F. Martingales and Counting Processes. Appendix G. Data Sets. References.
£144.85
Taylor & Francis Inc Winning Ways for Your Mathematical Plays: Volume
Book SynopsisThis classic on games and how to play them intelligently is being re-issued in a new, four volume edition. This book has laid the foundation to a mathematical approach to playing games. The wise authors wield witty words, which wangle wonderfully winning ways. In Volume 1, the authors do the Spade Work, presenting theories and techniques to "dissect" games of varied structures and formats in order to develop winning strategies.Trade Review" ""Winning Ways is an absolute must have for those who are interested in mathematical game theory. It is sure to please any fan of recreational mathematics or simply anyone who is interested in games and how to play them well."" -Jacob McMillen, Math Horizons, November 2005 ""This new edition confirms the status of the book as a standard reference, which it will continue to be for at least another decade."" -Adhemar Bultheel, Bulletin of the Belgian Mathematical Society , December 2005"Table of ContentsPreface to Second Edition, Preface, Spade-Work!, 1. WhoseGame?, 2. Finding the Correct Number is Simplicity Itself, 3. Some Harder Games and How to Make Them Easier, 4. Taking and Breaking, 5. Numbers, Nimbers and Numberless Wonders, 6. The Heat of Battle, 7. Hackenbush, 8. It’s a Small Small Small Small World, Index
£62.99
CRC Press Risk Assessment and Decision Analysis with
Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary
£42.74
Cambridge University Press Examples in Finite Differences Calculus and Probability
a huge range and FREE tracked UK delivery on ALL orders.
£19.99
Cambridge University Press New Cambridge Statistical Tables
Book SynopsisThe second edition of this very successful and authoritative set of tables still benefits from clear typesetting, which makes the figures easy to read and use. It has, however, been improved by the addition of new tables that provide Bayesian confidence limits for the binomial and Poisson distributions, and for the square of the multiple correlation coefficient, which have not been previously available. The intervals are the shortest possible, consistent with the requirement on probability. Great care has been taken to ensure that it is clear just what is being tabulated and how the values may be used; the tables are generally capable of easy interpolation. The book contains all the tables likely to be required for elementary statistical methods in the social, business and natural sciences. It will be an essential aid for teachers, researchers and students in those subjects where statistical analysis is not wholly carried out by computers.Trade Review'This is an excellent book offered at an unusually low price of £3.50. Any forensic scientist who analyses data will be well advised to ensure that a copy is always close to hand.' Journal of the Forensic Science Society' … very extensive...clear well explained tables.' P. J. Avery, British Journal of Biomedical Science' … these are among the best available and they are well set out.' P. Sprent, Journal of Applied EcologyTable of Contents1. The binomial distribution function; 2. The Poisson distribution function; 3. Binomial coefficients; 4. The normal distribution function; 5. Percentage points of the normal distribution; 6. Logarithms of factorials; 7. The chi-squared distribution function; 8. Percentage points of the chi-squared distribution; 9. The t-distribution function; 10. Percentage points of the t-distribution; 11. Percentage points of Behrens' distribution; 12. Percentage points of the F-distribution; 13. Percentage points of the correlation coefficient r when rho = 0; 14. Percentage points of Spearman's S; 15. Percentage points of Kendall's K; 16. The z-transformation of the correlation coefficient; 17. The inverse of the z-transformation; 18. Percentage points of the distribution of the number of runs; 19. Upper percentage points of the two-sample Kolmogorov–Smirnov distribution; 20 Percentage points of Wilcoxon's signed-rank distribution; 21. Percentage points of the Mann–Whitney distribution; 22A. Expected values of normal order statistics (normal scores); 22B. Sums of squares of normal scores; 23. Upper percentage points of the one-sample Kolmogorov–Smirnov distribution; 24. Upper percentage points of Friedmann's distribution; 25. Upper percentage points of the Kruskal–Wallis distribution; 26. Hypergeometric probabilities; 27. Random sampling numbers; 28. Random normal deviates; 29. Bayesian confidence limits for a binomial parameter; 30. Bayesian confidence limits for a Poisson mean; 31. Bayesian confidence limits for the square of a multiple correlation coefficient; A note on interpolation; Constants.
£14.99
Elsevier Science & Technology Simulation
Book SynopsisTrade Review"This textbook contains and describes all the tools one needs to plan and to carry out a simulation study as well as to analyze its results." --J.Wolters, zbMATH Open "It presents the statistics needed to analyze simulated data and to validate the simulation model. In this edition, several new topics are included as well as a number of new exercises." --Vigirdas Mackevicius, zbMATH OpenTable of Contents1. Introduction 2. Elements of Probability 3. Random Numbers 4. Generating Discrete Random Variables 5. Generating Continuous Random Variables 6. The Multivariate Normal Distribution and Copulas 7. The Discrete Event Simulation Approach 8. Statistical Analysis of Simulated Data 9. Variance Reduction Techniques 10. Additional Variance Reduction Techniques 11. Statistical Validation Techniques 12. Markov Chain Monte Carlo Methods
£69.26
Oxford University Press Statistics of Extremes and Records in Random
Book SynopsisRare events such as earthquakes, tsunamis, and floods fortunately do not occur every day, but when they do, their effects are devastating. Such rare events are particularly important in understanding and characterizing global warming and climate changes. In addition to natural catastrophes, rare events such as big financial crashes also play a significant role in the economy. In the absence of predictive models, the best way forward is to analyse the statistics of these extreme events and draw conclusions about the probability of their occurrences.Extreme value statistics (EVS) and the statistics of records in a random sequence are examples of a truly interdisciplinary topic, spanning from statistics and mathematics on one side to physics of disordered systems on the other. They have tremendous importance and practical applications in a wide variety of fields, such as climate science, finance, spin-glasses, and random matrices.Statistics and mathematical literature have explored the su
£42.75
Oxford University Press A Modern Introduction to Probability and
Book SynopsisProbability and statistics are subjects fundamental to data analysis, which, in turn, is essential for efficient artificial intelligence.
£38.00
Oxford University Press Inc Explanation in Causal Inference
Book SynopsisTrade ReviewYes, mediation is an important topic. It has longed been used in the social sciences especially psychology. Of late there has been interest in many different fields including economics, sociology, epidemiology, political science and education, among other fields. Tyler VanderWeele is very qualified to author this book. He has contributed important work to the development of this topic and is a talented and careful researcher. I think there is potential for adoption in graduate courses in the social and biomedical sciences. I also think it could be widely purchased by applied researchers as a reference. I recommend publication. * Luke Keele, Associate Professor, Department of Political Science, Penn State University *Mediation is about understanding pathways between a treatment and an outcome that lead to the outcome, i.e., mechanisms. Mechanisms are a central thing in science and statisticians have been providing new principled methods for studying these topics over especially the last 10 years. Especially in the social and behavioral sciences and in epidemiology there has been great interest in these methods, and the methodology the author wants to write about is the new stuff from the last 10 years. [VanderWeele] is the key player in statistical literature these days. He's a good communicator… Primary market: applied researchers doing mediation in epidemiology, social and behavioral sciences. Secondary market: applied statisticians teaching causal inference and/or working in the area." " * Michael Sobel, Dept Sociology, Columbia *Table of ContentsPART I: MEDIATION ANALYSIS ; Chapter 1. Explanation and Mechanism ; Chapter 2. Mediation: Introduction and Regression-Based Approaches ; Chapter 3. Sensitivity Analysis for Mediation ; Chapter 4. Mediation Analysis with Survival Data ; Chapter 5. Multiple Mediators ; Chapter 6. Mediation Analysis with Time-Varying Exposures and Mediators ; Chapter 7. Selected Topics in Mediation Analysis ; Chapter 8. Other Topics Related to Intermediates ; PART II: INTERACTION ANALYSIS ; Chapter 9. An Introduction to Interaction Analysis ; Chapter 10. Mechanistic Interaction ; Chapter 11. Bias Analysis for Interactions ; Chapter 12. Interaction in Genetics: Independence and Boosting Power ; Chapter 13. Power and Sample-Size Calculations for Interaction Analysis ; PART III: SYNTHESIS AND SPILLOVER EFFECTS ; Chapter 14. A Unification of Mediation and Interaction ; Chapter 15. Social Interactions and Spillover Effects ; Chapter 16. Mediation and Interaction: Future and Context ; Appendix. Technical Details and Proofs ; References
£115.00
Oxford University Press An Introduction to Quantitative Finance
Book SynopsisThe quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types. This book gives an insight into financial engineering while building on introductory probability courses by detailing one of the most fascinating applications of the subject.Trade ReviewShort and to the point, uncluttered, unfancy, free of the faux rigor of most modern finance textbooks, written by a practitioner, that hits most of the essential principles of quantitative finance. * Emanuel Derman, author of My Life as a Quant *The author writes elegantly, and combines precision of expression with topical real-world examples in a way that makes this an exceptional work. * Frank Kelly, University of Cambridge *It is all too rare to find clear thinking, based on first principles, combined with practical understanding of financial markets. This is precisely what Stephen Blyth offers, drawing equally on his mathematical and statistical training and his career in quantitative finance. This book beautifully explains both the profound implications of no-arbitrage theory for the prices of fixed-income derivative securities, and also the pitfalls in practical applications. * John Y Campbell, Harvard University *Table of ContentsI INTRODUCTION AND PRELIMINARIES; II FORWARDS, SWAPS AND OPTIONS; III REPLICATION, RISK-NEUTRALITY AND THE FUNDAMENTAL THEOREM; IV INTEREST RATE OPTIONS; V THROUGH CONTINUOUS TIME
£42.99
Taylor & Francis Inc The Art Of Probability
Book SynopsisOffering accessible and nuanced coverage, Richard W. Hamming discusses theories of probability with unique clarity and depth. Topics covered include the basic philosophical assumptions, the nature of stochastic methods, and Shannon entropy. One of the best introductions to the topic, The Art of Probability is filled with unique insights and tricks worth knowing.Table of ContentsProbability * Introduction * Models in General * The Frequency Approach Rejected * The Single Event Model * Symmetry as the Measure of Probability * Independence * Subsets of a Sample Space * Conditional Probability * Randomness * Critique of the Model Some Mathematical Tools * Permutations * Combinations * The Binomial DistributionBernoulli Trials * Random Variables, Mean and the Expected Value * The Variance * The Generating Function * The Weak Law of Large Numbers * The Statistical Assignment of Probability * The Representation of Information Methods for Solving Problems * The Five Methods * The Total Sample Space and Fair Games * Enumeration * Historical Approach * Recursive Approach * Recursive Approach * The Method of Random Variables * Critique of the Notion of a Fair Game * Bernoulli Evaluation * Robustness * InclusionExclusion Principle Countably Infinite Sample Spaces * Introduction * Bernoulli Trials * On the Strategy to be Adopted * State Diagrams * Generating Functions of State Diagrams * Expanding a Rational Generating Function * Checking the Solution * Paradoxes Continuous Sample Spaces * A Philosophy of the Real Number System * Some First Examples * Some Paradoxes * The Normal Distribution * The Distribution of Numbers * Convergence to the Reciprocal Distribution * Random Times * Dead Times * Poisson Distribution in Time * Queing Theorem * Birth and Death Systems * Summary Uniform Probability Assignments Maximum Entropy * What is Entropy? * Shannons Entropy * Some Mathematical Properties of the Entropy Function * Some Simple Applications * The Maximum Entropy Principle Models of Probability * General Remarks * Maximum Likelihood in a Binary Choice * Von Mises Probability * The Mathematical Approach * The Statistical Approach * When The Mean Does Not Exist * Probability as an Extension of Logic * Di Finetti * Subjective Probability * Fuzzy Probability * Probability in Science * Complex Probability Some Limit Theorems * The Biomial Approximation for the case p=1/2 * Approximation by the Normal Distribution * Another Derivation of the Normal Distribution * Random Times * The Zipf Distribution * Summary An Essay on Simulation
£76.99
The University of Chicago Press How Our Days Became Numbered Risk and the Rise
Book SynopsisExplains how life insurance corporations shaped how we understand American life spans and Americans as risks
£24.70
Elsevier Science An Introductory Handbook of Bayesian Thinking
Book Synopsis
£51.26
CRC Press Theory of Spatial Statistics
Book SynopsisThis book presents a concise introduction to the theory underlying the analysis of the main types of spatial data. It includes examples to illustrate the topics, including R code for their implementation, as well as exercises to support course teaching and self-study. Trade Review"This book provides a concise and readable introduction to the three main areas of spatial statistics: random fields, areal data and spatial point processes. Although the focus is on the basic underlying theory, extensive analyses of real data are provided including R code. Suitable as a text or for self-study, each major chapter includes exercises and solutions. A valuable resource for students and researchers in statistics and related fields looking to learn some of the basic theory underlying spatial statistics."- Michael Stein, University of Chicago"The book is a concise introduction to spatial statistics mostly from a mathematical point of view. It devotes a chapter to each of the main classes of spatial statistics settings, point referenced data and interpolation, areal data and spatial point processes. Each chapter contain all the main definitions and theorem (with proofs) for relevant models, and some of the inference methods for each of these spatial statistics settings. The chapters end with worked through R-examples and nice and useful pointers to the literature. The book expect the reader to be both mathematical and statistical mature, and most examples are mathematical. I think this can be a nice introduction and reference book for PhD students specializing in spatial statics, and it can also work as a supporting textbook for a mathematically orientated master level course in spatial statistics."- Ingelin Steinsland, Norwegian University of Science and Technology"Theory of Spatial Statistics: A Concise Introduction is an excellent introductory resource to all three subfields in spatial statistics: geostatistics, areal data, and point processes. The book is well-organized and self-contained, covering the key knowledge of spatial statistics in a unified manner. It describes the mathematical foundations of the related statistical theory with rigorous proofs. Each chapter contains detailed illustrative examples using R packages to exemplify the methodologies applied to some well-known data sets. The book is suitable as a textbook for both graduate and advanced undergraduate students who want to learn the basis of the fast-growing areas of spatial statistics. I like the idea of providing exercises and detailed solutions so that readers can assess their learning outcomes. This book will also be of interest to practitioners of applied statistics from various disciplines as a reference book."- Yang Li, University of Minnesota Duluth"This text provides an excellent introduction to spatial statistics, including some important theoretical results, as well as practical implementation of the methodologies discussed. The modeling approaches are naturally separated into three groups depending on the type of data at hand, i.e., gridded, area unit and mapped point pattern data. The author has managed to incorporate in the text the most commonly used approaches in the literature, along with their corresponding applications. One particularly useful feature is the illustration of R packages to fit these models. Moreover, the inclusion of solutions to theoretical problems offers a nice resource to refer to and utilize in teaching graduate courses on spatial statistics and point processes. In addition, the theoretical results presented make for a nice blend between theory and application. Overall, the book is well written and will be a welcomed addition to the library of any researcher in spatial statistics."- Athanasios (Sakis) Christou Micheas, University of Missouri-Columbia"This book surveys the main topics in spatial statistics, including modeling random fields, variogram estimation, hierarchical models, and spatial point processes...It is amazing how much information van Lieshout is able to convey so concisely and compactly. She is simply masterful at explaining very difficult, intricate, and important concepts, models and statistical methods in an eloquent way...The chapters are wonderfully well organized and cover an ideal list of core topics in the statistical analysis of the most common and important forms of spatial data. Perhaps the best part of the book are the worked examples, which aid the reader new to this material and help crystalize what these statistical models and methods are prescribing...It is clear that an enormous amount of effort went into these worked examples, though as with the theoretical topics, van Lieshout explains everything so clearly and concisely that she makes the applications and R coding look easy, and in some cases almost trivial...(The book) is a remarkable fusion of the most important topics in the field, both theoretical and applied, presented beautifully and eloquently with the utmost care and precision, and so concisely that it all fits into a small handbook. I would strongly recommend this book for anyone teaching a one-semester graduate level course in spatial statistics."- Frederic P. Schoenberg, University of California at Los Angeles"This book provides a concise and readable introduction to the three main areas of spatial statistics: random fields, areal data and spatial point processes. Although the focus is on the basic underlying theory, extensive analyses of real data are provided including R code. Suitable as a text or for self-study, each major chapter includes exercises and solutions. A valuable resource for students and researchers in statistics and related fields looking to learn some of the basic theory underlying spatial statistics."- Michael Stein, University of Chicago"The book is a concise introduction to spatial statistics mostly from a mathematical point of view. It devotes a chapter to each of the main classes of spatial statistics settings, point referenced data and interpolation, areal data and spatial point processes. Each chapter contain all the main definitions and theorem (with proofs) for relevant models, and some of the inference methods for each of these spatial statistics settings. The chapters end with worked through R-examples and nice and useful pointers to the literature. The book expect the reader to be both mathematical and statistical mature, and most examples are mathematical. I think this can be a nice introduction and reference book for PhD students specializing in spatial statics, and it can also work as a supporting textbook for a mathematically orientated master level course in spatial statistics."- Ingelin Steinsland, Norwegian University of Science and Technology"Theory of Spatial Statistics: A Concise Introduction is an excellent introductory resource to all three subfields in spatial statistics: geostatistics, areal data, and point processes. The book is well-organized and self-contained, covering the key knowledge of spatial statistics in a unified manner. It describes the mathematical foundations of the related statistical theory with rigorous proofs. Each chapter contains detailed illustrative examples using R packages to exemplify the methodologies applied to some well-known data sets. The book is suitable as a textbook for both graduate and advanced undergraduate students who want to learn the basis of the fast-growing areas of spatial statistics. I like the idea of providing exercises and detailed solutions so that readers can assess their learning outcomes. This book will also be of interest to practitioners of applied statistics from various disciplines as a reference book."- Yang Li, University of Minnesota Duluth"This text provides an excellent introduction to spatial statistics, including some important theoretical results, as well as practical implementation of the methodologies discussed. The modeling approaches are naturally separated into three groups depending on the type of data at hand, i.e., gridded, area unit and mapped point pattern data. The author has managed to incorporate in the text the most commonly used approaches in the literature, along with their corresponding applications. One particularly useful feature is the illustration of R packages to fit these models. Moreover, the inclusion of solutions to theoretical problems offers a nice resource to refer to and utilize in teaching graduate courses on spatial statistics and point processes. In addition, the theoretical results presented make for a nice blend between theory and application. Overall, the book is well written and will be a welcomed addition to the library of any researcher in spatial statistics."- Athanasios (Sakis) Christou Micheas, University of Missouri-Columbia"This book surveys the main topics in spatial statistics, including modeling random fields, variogram estimation, hierarchical models, and spatial point processes...It is amazing how much information van Lieshout is able to convey so concisely and compactly. She is simply masterful at explaining very difficult, intricate, and important concepts, models and statistical methods in an eloquent way...The chapters are wonderfully well organized and cover an ideal list of core topics in the statistical analysis of the most common and important forms of spatial data. Perhaps the best part of the book are the worked examples, which aid the reader new to this material and help crystalize what these statistical models and methods are prescribing...It is clear that an enormous amount of effort went into these worked examples, though as with the theoretical topics, van Lieshout explains everything so clearly and concisely that she makes the applications and R coding look easy, and in some cases almost trivial...(The book) is a remarkable fusion of the most important topics in the field, both theoretical and applied, presented beautifully and eloquently with the utmost care and precision, and so concisely that it all fits into a small handbook. I would strongly recommend this book for anyone teaching a one-semester graduate level course in spatial statistics."- Frederic P. Schoenberg, University of California at Los AngelesTable of Contents1. Introduction. 2. Random field modelling and interpolation. 3. Models and inference for areal unit data. 4. Spatial point processes. Appendix: Solutions to theoretical exercises
£58.99
Taylor & Francis An Introduction to Multilevel Modeling Techniques
Book SynopsisMultilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; <Trade Review"Developing a basic modeling strategy that researchers can follow to investigate multilevel data structures can be challenging. Heck and Thomas have once again presented a must-have reference book to get the job done. This edition’s use of four different software packages and additional easy-to-follow illustrative examples enhance what was already a superb resource for both students and researchers." – George A. Marcoulides, University of California, Santa Barbara, USA Table of ContentsPreface 1. Introduction 2. Getting Started with Multilevel Analysis 3. Multilevel Regression Models 4. Extending the Two-Level Regression Model 5. Methods for Examining Individual and Organizational Change 6. Multilevel Models with Categorical Variables 7. Multilevel Structural Equation Variables 8. Multilevel Latent Growth and Mixture Models 9. Data Consideration in Examining Multilevel Models
£54.99
Taylor & Francis Ltd Understanding Regression Analysis A Conditional
Book SynopsisUnderstanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters <Trade Review"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."- Ryan Andrews, ISCB News, June 2021 Table of Contents1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend
£120.00
Taylor & Francis Ltd SPSS Demystified
Book SynopsisWithout question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of studentsâ anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS . Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses.Key features of the text:â Step-by-step instruction and screenshotsâ Designed to be hands-on with the user performing the analyses alongside the text on their computer as they read through each chapterâ Call-out boxes provided, highlighting important information as appropriateâ SPSS output explained, with written results provided using the popular, widely recognized APA formatâ End-of-chapter exercises included, allowing for additional practiceâ SPSS data sets available on the publisherâs websiteNew to the Fourth Edition:â Fully updated to SPSS 28â Updated screenshots in full color to reflect changes in the SPSS software system (version 28)â Exercises updated with up-to-date examplesâ Exact p-values provided (consistent with APA recommendations)Table of ContentsPart I: Introduction to SPSS, Descriptive Statistics, Graphical Procedures of Data, and Reliability Using Coefficient Alpha 1. Introduction to SPSS, 2. Descriptive Statistics: Frequencies, Measures of Central Tendency, and Measures of Variability, 3. Graphical Procedures, 4. Reliability (As Measured by Coefficient Alpha); Part II: Inferential Statistics 5. The One-Sample t Test, 6. The Independent-Samples t Test, 7. The Dependent-Samples t Test, 8. The One-Way Between Subjects Analysis of Variance (ANOVA), 9. The Two-Way Between Subjects Analysis of Variance (ANOVA), 10. The One-Way Within Subjects Analysis of Variance (ANOVA), 11. The One-Between–One-Within Subjects Analysis of Variance (ANOVA), 12. The Pearson r Correlation Coefficient, 13. Simple Linear Regression, 14. Multiple Linear Regression, 15. The Chi-Square Goodness of Fit Test, 16. The Chi-Square Test of Independence; Appendix A. Data Transformations and Other Procedures Appendix B. Solutions to Chapter Exercises
£52.24
Taylor & Francis Interpreting Basic Statistics
Book SynopsisInterpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. Features of the ninth edition: Covers a broad array of basic statistical concepts, including topics drawn from the New Statistics Up-to-date journal excerpts reflecting contemporary styles in statistical reporting Strong emphasis on data visualization Ancillary materials include data sets with almost two hours of accompanying tutorial videos, which will help students and instructors apply lessons from the book to real-life scenarios About this book Each of the 63 exercises in the book contain three central components: 1) an introduction to a statistical concept, 2) a brief excerpt from a published research article that uses the statistical concept, and 3) a set of questions (with answers) that guides students into deeper learning about the concept. The questioTrade ReviewThe 9th edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USAThe ninth edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USATable of Contents1. Basic Descriptions of the Data: Measurement and Frequency 2. Describing the Data 3. Displaying Data: Visualizing What is There 4. Finding Relationships: Association and Prediction 5. Group Differences with Normal Distributions 6. Nonparametric Tests for Group Differences 7. Test Construction
£171.00
CRC Press Medical Risk Prediction Models
Book SynopsisMedical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patientâs individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest.Features: All you need to know to correctly make an online risk calculator from scratch. Discrimination, calibration, and predictive performance with censored data and competing risks. R-code and illustrative examples. InterpreTrade Review"Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."~Donna Ankerst, Technical University of Munich "Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book."~Donna Ankerst, Technical University of Munich "Overall, the book offers a well-written, complete and illustrative overview of clinical prediction models with clear stances and directions on the modelling methods, choices and strategies. I find this a very welcome and much needed addition to the literature because prediction is the backbone of medical decision-making; few books are dedicated to modelling strategies and artificial intelligence is ascending in medical research. I thereby highly recommend this book for anyone who would be interested in performing predictive modelling for prognostic or diagnostic research." -Evangelos I. Kritsotakis, International Society for Clinical Biostatistics, 72, 2021 Table of Contents Software. 2. I am going to make a prediction model. What do I need to know? 3. Regression model. 4. How should I prepare for modeling? 5. I am ready to build a prediction model. 7. Does my model predict accurately? 7. How do I decide between rival models? 8. Can't the computer just take care of all of this? 9. Things you might have expected in our book.
£49.99
Taylor & Francis Ltd Crime Mapping and Spatial Data Analysis Using R
Book SynopsisPractical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.Trade Review"I think overall the book is pitched perfectly and the step by step approach with code will act as an excellent training resources as well as reference guide.”-Ruth Weir, City, University of London"Overall, this is a great book! It is written in an accessible style, is up to date and covers the foundational material one would want a student to understand. As an experienced R user, I was delighted to learn something. Staying abreast of the fast-developing packages is nearly a full-time job, so I see this book as highly useful to many readers. The authors do a great job illustrating the main concepts of import but also pointing readers to places to follow up for more detailed treatments.”-Michael Townsley, Professor of Criminology and Criminal Justice, Griffith UniversityTable of Contents1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis
£73.14
Taylor & Francis Ltd Big Data Systems
Book SynopsisBig Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples.Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples Table of ContentsPreface Author Bios Acknowledgements List of Figures List of Tables Introduction to Big Data Systems 1.1 INTRODUCTION: REVIEW OF BIG DATA SYSTEMS1.2 UNDERSTANDING BIG DATA 1.3 TYPE OF DATA: TRANSACTIONAL OR ANALYTICAL1.4 REQUIREMENTS AND CHALLENGES OF BIG DATA 1.5 CONCLUDING REMARKS 1.6 FURTHER READING 1.7 EXERCISE QUESTIONS Architecture and Organization of Big Data Systems 2.1 ARCHITECTURE FOR BIG DATA SYSTEMS 2.2 ORGANIZATION OF BIG DATA SYSTEMS: CLUSTERS2.3 CLASSIFICATION OF CLUSTERS: DISTRIBUTED MEMORY VS. SHARED MEMORY2.4 CONCLUDING REMARKS 2.5 FURTHER READING 2.6 EXERCISE QUESTIONS Cloud Computing for Big Data 3.1 CLOUD COMPUTING 3.2 VIRTUALIZATION 3.3 PROCESSOR VIRTUALIZATION 3.4 CONTAINERIZATION 3.5 VIRTUALIZATION OR CONTAINERIZATION 3.6 FOG COMPUTING 3.7 EXAMPLES 3.8 CONCLUDING REMARKS 3.9 FURTHER READING 3.10 EXERCISE QUESTIONS HADOOP: An Efficient Platform for Storing and Processing Big Data 4.1 REQUIREMENTS FOR PROCESSING AND STORING BIG DATA 4.2 HADOOP - THE BIG PICTURE 4.3 HADOOP DISTRIBUTED FILE SYSTEM 4.4 MAPREDUCE 4.5 HBASE 4.6 CONCLUDING REMARKS 4.7 FURTHER READING 4.8 EXERCISE QUESTIONS Enhancements in Hadoop 5.1 ISSUES WITH HADOOP 5.2 YARN 5.3 PIG 5.4 HIVE 5.5 DREMEL 5.6 IMPALA 5.7 DRILL 5.8 DATA TRANSFER 5.9 AMBARI 5.10 CONCLUDING REMARKS 5.11 FURTHER READING 5.12 EXERCISE QUESTIONS Spark 6.1 LIMITATIONS OF MAPREDUCE 6.2 INTRODUCTION TO SPARK 6.3 SPARK CONCEPTS 6.4 SPARK SQL 6.5 SPARK MLLIB 6.6 STREAM BASED SYSTEM 6.7 SPARK STREAMING 6.8 CONCLUDING REMARKS 6.9 FURTHER READING 6.10 EXERCISE QUESTIONS NoSQL Systems 7.1 INTRODUCTION 7.2 HANDLING BIG DATA SYSTEMS - PARALLEL RDBMS 7.3 EMERGENCE OF NOSQL SYSTEMS 7.4 KEY-VALUE DATABASE 7.5 DOCUMENT-ORIENTED DATABASE 7.6 COLUMN-ORIENTED DATABASE 7.7 GRAPH DATABASE 7.8 CONCLUDING REMARKS 7.9 FURTHER READING 7.10 EXERCISE QUESTIONS NewSQL Systems 8.1 INTRODUCTION8.2 TYPES OF NEWSQL SYSTEMS 8.3 FEATURES 8.4 NEWSQL SYSTEMS: CASE STUDIES 8.5 CONCLUDING REMARKS 8.6 FURTHER READING8.7 EXERCISE QUESTIONS Networking for Big Data 9.1 NETWORK ARCHITECTURE FOR BIG DATA SYSTEMS9.2 CHALLENGES AND REQUIREMENTS 9.3 NETWORK PROGRAMMABILITY AND SOFTWARE DEFINED NETWORKING 9.4 LOW LATENCY AND HIGH SPEED DATA TRANSFER9.5 AVOIDING TCP INCAST - ACHIEVING LOW LATENCYAND HIGH THROUGHPUT 9.6 FAULT TOLERANCE9.7 CONCLUDING REMARKS 9.8 FURTHER READING 9.9 EXERCISE QUESTIONS Security for Big Data 10.1 INTRODUCTION 10.2 SECURITY REQUIREMENTS 10.3 SECURITY: ATTACK TYPES AND MECHANISMS 10.4 ATTACK DETECTION AND PREVENTION 10.5 CONCLUDING REMARKS 10.6 FURTHER READING 10.7 EXERCISE QUESTIONS Privacy for Big Data 11.1 INTRODUCTION 11.2 UNDERSTANDING BIG DATA AND PRIVACY 11.3 PRIVACY VIOLATIONS AND THEIR IMPACT 11.4 TYPES OF PRIVACY VIOLATIONS 11.5 PRIVACY PROTECTION SOLUTIONS AND THEIR LIMITATIONS 11.6 CONCLUDING REMARKS 11.7 FURTHER READING 11.8 EXERCISE QUESTIONS High Performance Computing for Big Data 12.1 INTRODUCTION 12.2 SCALABILITY: NEED FOR HPC 12.3 GRAPHIC PROCESSING UNIT 12.4 TENSOR PROCESSING UNIT 12.5 HIGH SPEED INTERCONNECTS 12.6 MESSAGE PASSING INTERFACE 12.7 OPENMP 12.8 OTHER FRAMEWORKS 12.9 CONCLUDING REMARKS 12.10 FURTHER READING 12.11 EXERCISE QUESTIONS Deep Learning with Big Data 13.1 INTRODUCTION 13.2 FUNDAMENTALS 13.3 NEURAL NETWORK 13.4 TYPES OF DEEP NEURAL NETWORK 13.5 BIG DATA APPLICATIONS USING DEEP LEARNING13.6 CONCLUDING REMARKS 13.7 FURTHER READING 13.8 EXERCISE QUESTIONS Big Data Case Studies 14.1 GOOGLE EARTH ENGINE 14.2 FACEBOOK MESSAGES APPLICATION 14.3 HADOOP FOR REAL-TIME ANALYTICS 14.4 BIG DATA PROCESSING AT UBER 14.5 BIG DATA PROCESSING AT LINKEDIN 14.6 DISTRIBUTED GRAPH PROCESSING AT GOOGLE 14.7 FUTURE TRENDS 14.8 CONCLUDING REMARKS 14.9 FURTHER READING 14.10 EXERCISE QUESTIONS Bibliography Index
£44.99
Taylor & Francis Ltd Applications of Regression for Categorical
Book SynopsisThis book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy Table of Contents1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data
£139.50
Taylor & Francis Ltd A Tour of Data Science
Book SynopsisA Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source.Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn progrTable of ContentsAssumptions about the reader’s backgroundBook overview Introduction to R/Python Programming Calculator Variable and TypeFunctions Control flowsSome built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategySpeed up with C/C++ in R/PythonA first impression of functional programming Miscellaneous data.table and pandasSQL Get started with data.table and pandas Indexing & selecting data Add/Remove/UpdateGroup by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence intervalHypothesis testing Basics of linear regression Ridge regression Optimization in PracticeConvexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous
£123.50
Springer New York Model Selection and Multimodel Inference A
Book SynopsisA unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data.Table of ContentsIntroduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary
£143.99
Springer-Verlag New York Inc. Statistical Consulting
Book SynopsisI The Methodology of Statistical Consulting.- 1 Introduction to Statistical Consulting.- 2 Communication.- 3 Methodological Aspects.- 4 A Consulting Project from A to Z.- II Case Studies.- 5 Introduction to the Case Studies.- 6 Case Studies from Group I.- 7 Case Studies from Group II.- 8 Case Studies from Group III.- 9 Additional Case Studies.- A Resources.- A.1 References.- A.2 Datasets for Case Studies in Part II.- A.3 Statistical Consulting Course.- A.3.1 Course Description.- A.3.2 List of Topics by Week.- A.3.3 Reference List.- B Statistical Software.- B.1 SAS.- B.1.1 The SAS Setup.- B.1.2 Details on the DATA Step.- B.1.3 SAS Procedures.- B.1.4 Further Details of SAS.- B.2 S-PLUS.- B.2.1 S-PLUS Preliminaries.- B.2.2 The S-PLUS Setup.- B.2.3 Basic S-PLUS Commands.- B.2.4 Efficient Use of S-PLUS.- B.2.5 S-PLUS Statistical Procedures.- B.2.6 S-PLUS Glossary.- C Statistical Addendum.- C.1 Univariate Distributions.- C.2 Multivariate Distributions.- C.3 Statistical Tests.- C.4 Sample SizTrade ReviewFrom the reviews: THE AMERICAN STATISTICIAN "Although there are other books that effectively tackle the individual aspects described above, this book seems to be the most ideally suited to teaching a well-rounded statistics course at the undergraduate of graduate level…[It] gives informative and self-contained discussions for the many aspects of consulting in balanced proportions that would make using the book for a textbook delightfully straightforward. The collection of case studies is diverse in disciplines considered and level of difficulty, and seems to focus on interesting problems that students will find highly motivating…a valuable resource for statistical consultants, both beginning and established…a prime candidate for use as a stand-alone textbook…since it contains a desirable balance of materials with statistical methodology, oral and written communication skills, and rich case studies…It will make a solid long-term reference for students. Also, for instructors of more traditional senior undergraduate and junior graduate courses, it provides useful case studies to illustrate standard methods in realistic settings that can easily be implemented."Table of ContentsIntroduction to Statistical Consulting * Communication * Methodological Aspects * A Consulting Project from A to Z * Introduction to Case Studies * Case Studies from Group I * Case Studies from Group II * Case Studies from Group III * Additional Case Studies
£89.99
CRC Press Statistical Theory
Book SynopsisThis classic textbook is suitable for a first course in the theory of statistics for students with a background in calculus, multivariate calculus, and the elements of matrix algebra.Table of ContentsPreface, 1 Preliminaries, 2 Probability, 3 Random Variables, 4 Expectations, 5 Limit Theorems, 6 Some Parametric Families, 7 Sampling and Reduction of Data, 8 Estimation, 9 Testing Hypotheses, 10 Analysis of Categorical Data, 11 Sequential Analysis, 12 Multivariate Distributions, 13 Nonpararnetric Tests, 14 Linear Models and Analysis of Variance, 15 Decision Theory, Tables, References and Further Reading, Answers to Problems, Index
£147.25
Taylor & Francis Statistics for Sport and Exercise Studies
Book SynopsisStatistics for Sport and Exercise Studies guides the student through the full research process, from selecting the most appropriate statistical procedure, to analysing data, to the presentation of results, illustrating every key step in the process with clear examples, case-studies and data taken from real sport and exercise settings.Every chapter includes a range of features designed to help the student grasp the underlying concepts and relate each statistical procedure to their own research project, including definitions of key terms, practical exercises, worked examples and clear summaries. The book also offers an in-depth and practical guide to using SPSS in sport and exercise research, the most commonly used data analysis software in sport and exercise departments. In addition, a companion website includes more than 100 downloadable data sets and work sheets for use in or out of the classroom, full solutions to exercises contained in the book, plus over 1,300 PoTable of Contents1. Data, Information and Statistics 2. Using this book 3. Descriptive Statistics 4. Standardized scores 5. Probability 6. Data distributions 7. Hypothesis testing 8. Correlation 9. Linear Regression 10. t Tests 11. Analysis of Variances 12. Factorial ANOVA 13. Multivariate ANOVA 14. Nonparametric tests 15. Chi Square 16. Statistical Classification 17. Cluster Analysis 18. Data Reduction using Principal Components Analysis 19. Reliability 20. Statistical Power
£51.29
Elsevier Science Computational Analysis and Understanding of
Book SynopsisTable of Contents1. Linguistics: Core Concepts and Principles 2. Grammars 3. Open-Source Libraries, Application Frameworks, Workflow Systems, and Other Resources 4. Mathematical Essentials 5. Probability 6. Inference and Prediction Methods 7. Random Processes 8. Bayesian Methods 9. Machine Learning 10. Artificial Neural Networks for Natural Language Processing 11. Information Retrieval 12. Language Core Tasks 1 13. Language Core Tasks 2 14. Language Understanding Applications 1 15. Language Understanding Applications 2 16. Deep Learning for Natural Language Processing 17. Text Mining for Modeling Cyberattacks 18. World Languages and Crosslinguistics 19. Linguistic Elegance of the Languages of South India 20. Current Trends and Open Problems
£180.00
John Wiley & Sons Inc Basic Statistics for Social Research
Book SynopsisBasic Statistics for Social Research offers an introduction to core general statistical concepts and methods. It covers procedural aspects of the application of statistical methods for data-description; and hypothesis-testing; distributions, tabulations, central tendency, variability, independence, correlation and regression.Table of ContentsTables and Figures ix Preface xv About the Authors xix Part I Univariate Description 1 Chapter 1 Using Statistics 3 Why Study Statistics? 4 Tasks for Statistics: Describing, Inferring, Testing, Predicting 4 Statistics in the Research Process 9 Basic Elements of Research: Units of Analysis and Variables 14 Chapter 2 Displaying One Distribution 25 Summarizing Variation in One Variable 26 Frequency Distributions for Nominal Variables 26 Frequency Distributions for Ordinal Variables 32 Frequency Distributions for Interval/Ratio Variables 38 Summarizing Data Using Excel 43 Chapter 3 Central Tendency 81 The Basic Idea of Central Tendency 82 The Mode 83 The Median 88 The Mean 95 Chapter 4 Dispersion 113 The Basic Idea of Dispersion 114 Dispersion of Categorical Data 115 Dispersion of Interval/Ratio Data 121 Chapter 5 Describing the Shape of a Distribution 149 The Basic Ideas of Distributional Shape 150 The Shape of Nominal and Ordinal Distributions 152 Unimodality 158 Skewness 163 Kurtosis 169 Some Common Distributional Shapes 175 Chapter 6 The Normal Distribution 187 Introduction to the Normal Distribution 188 Properties of Normal Distributions 189 The Standard Normal, or Z, Distribution 192 Working with Standard Normal (Z) Scores 194 Finding Areas “Under the Curve” 197 Part II Inference and Hypothesis Testing 209 Chapter 7 Basic Ideas of Statistical Inference 211 Introduction to Statistical Inference 212 Sampling Concepts 214 Central Tendency Estimates 219 Assessing Confidence in Point Estimates 229 Chapter 8 Hypothesis Testing for One Sample 247 Hypothesis Testing 248 The Testing Process 250 Tests about One Mean 258 Tests about One Proportion 267 Chapter 9 Hypothesis Testing for Two Samples 279 Comparing Two Groups 280 Comparing Two Groups’ Means 280 Comparing Two Groups’ Proportions 289 Non independent Samples 296 Using Excel for Two-Sample Tests 301 Interpreting Group Differences 302 Chapter 10 Multiple Sample Tests of Proportions: Chi-Squared 313 Comparing Proportions across Several Groups 314 Testing for Multiple Group Differences 315 Describing Group Differences 327 Chapter 11 Multiple Sample Tests for Means: One-Way ANOVA 337 Comparing Several Group Means with Analysis of Variance 338 Analyzing Variance and the F-Test 339 Analyzing Variance 342 The F-Test 350 Comparing Means 356 Part III Association and Prediction 369 Chapter 12 Association with Categorical Variables 371 The Concept of Statistical Association 372 Association with Nominal Variables 375 Association with Ordinal Variables 391 Chapter 13 Association of Interval/Ratio Variables 425 Visualizing Interval/Ratio Association 426 Significance Testing for Interval/Ratio Association 434 Chapter 14 Regression Analysis 453 Predicting Outcomes with Regression 454 Simple Linear Regression 454 Applying Simple Regression Analysis 465 Multiple Regression 469 Applying Multiple Regression 474 Chapter 15 Logistic Regression Analysis 489 Predicting with Nonlinear Relationships 490 Logistic Regression 492 The Logistic Regression Model 492 Interpreting Effects in Logistic Regression 493 Estimating Logistic Regression Models with Maximum Likelihood 495 Applying Logistic Regression 496 Assessing Partial Effects 498 Extending Logistic Regression 499 Appendix Chi-Squared Distribution: Critical Values for Commonly Used Alpha=0.05 and Alpha=0.01 505 F-Distribution: Critical Values for Commonly Used Alpha=0.05 and Alpha=0.01 507 Standard Normal Scores (Z-Scores), and Cumulative Probabilities (Proportion of Cases Having Scores below Z) 511 Student’s t-Distribution: Critical Values for Commonly Used Alpha Levels 517 Index 519
£77.95
Wiley-Blackwell Spatiotemporal Design Advances in Efficient Data Acquisition
a huge range and FREE tracked UK delivery on ALL orders.
£74.66
Cambridge University Press Introduction to Measure and Integration
Book SynopsisThis paperback, which comprises the first part of Introduction to Measure and Probability by J. F. C. Kingman and S. J. Taylor, gives a self-contained treatment of the theory of finite measures in general spaces at the undergraduate level. It sets the material out in a form which not only provides an introduction for intending specialists in measure theory but also meets the needs of students of probability. The theory of measure and integration is presented for general spaces, with Lebesgue measure and the Lebesgue integral considered as important examples whose special properties are obtained. The introduction to functional analysis which follows covers the material to probability theory and also the basic theory of L2-spaces, important in modern physics. A large number of examples is included; these form an essential part of the development.Table of ContentsPreface; 1. Theory of sets; 2. Point set topology; 3. Set functions; 4. Construction and properties of measure; 5. Definitions and properties of the integral; 6. Related Spaces and measures; 7. The space of measurable functions; 8. Linear functionals; 9. Structure of measures in special spaces; Index of notation; General index.
£43.19
Cambridge University Press Table of D and
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£35.14
Cambridge University Press Asymptotic Efficiency of Nonparametric Tests
Book SynopsisThis monograph is the first unified treatment of an indispensable technique for comparing statistical tests, especially in nonparametric statistics. It presents powerful new methods to evaluate explicitly different kinds of efficiencies. Many Russian results are published here for the first time in English.Trade Review'It is an excellent book. I believe that every mathematical statistician should have this book in his or her collection … I enjoyed reading this book. I am sure that others will also like it.' Ramalingam Shanmugam, SIAM ReviewsTable of ContentsIntroduction; 1. Asymptotic efficiency of statistical tests and mathematical means for its computation; 2. Asymptotic efficiency of nonparametric goodness-of-fit tests; 3. Asymptotic efficiency of nonparametric homogeneity tests; 4. Asymptotic efficiency of nonparametric symmetry tests; 5. Asymptotic efficiency of nonparametric independence tests; 6. Local asymptotic optimality of nonparametric tests and the characterisation of distributions.
£98.80
Cambridge University Press Essentials of Statistical Inference
Book SynopsisWritten for advanced undergraduates and graduate students in mathematics and related disciplines, this book explains the main approaches to statistical inference, with particular emphasis on the contrasts between them. It is the first textbook to synthesize material on computational topics with basic mathematical theory. Each chapter includes instructive problems.Trade Review'This is a delightful book! It gives a well-written exposure to inference issues in statistics, very suitable for a first-year graduate course … The authors present the material in a very good pedagogical manner. The examples are excellent, and the exercises are very instructive … very much up to date and includes recent developments in the field.' MAA Reviews'This is a solid book, ideal for advanced classes in the mathematical justification for statistical inference.' Journal of Recreational Mathematics'I wish that I had had such a textbook during my student days … this new book presents the core ideas of statistical inference in the unifying framework of decision theory and includes a fruitful discussion of the different foundational standpoints (Bayesian, Fisherian and frequentist) … [it is] sufficiently precise to satisfy a mathematician and yet omitting too much technical detail that could hide the core of the ideas. Carefully selected examples from a rainbow of application areas such as baseball, coal-mining disasters or gene expression data make it even more enjoyable to read … this book is a very nice graduate level textbook.' Journal of the Royal Statistical Society'[This] book gives a clear and comprehensive account of the basic elements of statistical theory. It should make a good text for an advanced course on statistical inference … Students will find it informative and challenging.' ISI Short Book Reviews'Essentials of Statistical Inference is a book worth having.' Jane L. Harvill, Journal of the American Statistical Association'The book is comprehensively written without dwelling in unnecessary details.' Iris Pigeot, Biometrics'… gives a clear and comprehensive account of the basic elements of statistical theory … a good text for an advanced course on statistical inference.' Publication of the International Statistical Institute'The text presents the main concepts and results underlying different frameworks of inference, with particular emphasis on the contrasts among frequentist, Fisherian, and Bayesian approaches. It provides a depiction of basic material on these main approaches to inference, as well as more advanced material on recent developments in statistical theory, including higher-order likelihood inference, bootstrap methods, conditional inference, and predictive inference.' Zentralblatt MATHTable of Contents1. Introduction; 2. Decision theory; 3. Bayesian methods; 4. Hypothesis testing; 5. Special models; 6. Sufficiency and completeness; 7. Two-sided tests and conditional inference; 8. Likelihood theory; 9. Higher-order theory; 10. Predictive inference; 11. Bootstrap methods.
£34.99
Cambridge University Press Markov Chains 2 Cambridge Series in Statistical
Book SynopsisA textbook for students with some background in probability that develops quickly a rigorous theory of Markov chains and shows how actually to apply it, e.g. to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice.Trade Review'This is an admirable book, treating the topic with mathematical rigour and clarity, mixed with helpful informality; and emphasising numerous applications to a wide range of subjects.' D. V. Lindley, The Mathematical Gazette'My overall impression of this book is very positive … this is the best introduction to the subject that I have come across.' Contemporary Physics'An instructor looking for a suitable text, at the level of a Master of Mathematics degree, can use this book with confidence and enthusiasm.' John Haigh, University of Sussex'We recently based a seminar on this book … it is well suited for an elementary, technically modest, but still rigorous introduction into the heart of a lively and relevant area of stochastic processes.' M. Scheutzow, Zentralblatt MATHTable of ContentsIntroduction; 1. Discrete-time Markov chains; 2. Continuous-time Markov chains I; 3. Continuous-time Markov chains II; 4. Further theory; 5. Applications; Appendix; Probability and measure; Index.
£37.99
Cambridge University Press Statistical Models
Book SynopsisThis lively and engaging book explains the basic ideas of association and regression, and tells you the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own.Trade Review'At last, a second course in statistics that is serious, correct, and interesting. The book teaches regression, causal modeling, maximum likelihood, and the bootstrap. Everyone who analyzes real data should read this book.' Persi Diaconis, Stanford University'This book is outstanding for the clarity of its thought and writing. It prepares readers for a critical assessment of the technical literature in the social and health sciences, and provides a welcome antidote to the standard formulaic approach to statistics.' Erich L. Lehmann, University of California, Berkeley'In Statistical Models, David Freedman explains the main statistical techniques used in causal modeling - and where the skeletons are buried. Complex statistical ideas are clearly presented and vividly illustrated with interesting examples. Both newcomers and practitioners will benefit from reading this book.' Alan Krueger, Princeton University'Regression techniques are often applied to observational data with the intent of drawing causal conclusions. In what circumstances is this justified? What are the assumptions underlying the analysis? Statistical Models answers these questions. The book is essential reading for anybody who uses regression to do more than summarize data. The treatment is original, and extremely well written. Critical discussions of research papers from the social sciences are most insightful. I highly recommend this book to anybody who engages in statistical modeling, or teaches regression, and most certainly to all of my students.' Aad van der Vaart, Vrije Universiteit Amsterdam'A pleasure to read, Statistical Models shows the field's most elegant writer at the height of his powers. While most textbooks hurry past core assumptions in order to explicate technique, this book places the spotlight on the core assumptions, challenging readers to think critically about how they are invoked in practice.' Donald Green, Yale University'Statistical Models, a modern introduction to the subject, discusses graphical models and simultaneous equations among other topics. There are plenty of instructive exercises and computer labs. Especially valuable is the critical assessment of the main 'philosophers's stones' in applied statistics. This is an inspiring book and a very good read, for teachers as well as students.' Gesine Reinert, Oxford University'Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation.' Mathematical ReviewsTable of Contents1. Observational studies and experiments; 2. The regression line; 3. Matrix algebra; 4. Multiple regression; 5. Multiple regression: special topics; 6. Path models; 7. Maximum likelihood; 8. The bootstrap; 9. Simultaneous equations; 10. Issues in statistical modeling.
£47.49
Cambridge University Press Independent Component Analysis
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£108.30
Cambridge University Press Essentials of Statistical Inference
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£75.04
Cambridge University Press Quantile Regression 38 Econometric Society Monographs
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£90.25
