Probability and statistics Books
Taylor & Francis Inc Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition
Book SynopsisWhile there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration.Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.Trade Review"The new edition of the book, with its updated and additional materials, is still a great choice as at textbook for Bayesian computation and inference courses in a graduate program in computational and applied statistics. It will also be considered as one of the best textbooks for a Bayesian computational course to nonstatisticians, including social scientists and engineers." – Debajyoti Sinha, Florida State University, in JASA, March 2009“The second edition of this book is well written and builds on the first edition … The addition of an associated website is a valuable resource that contains many R scripts, allowing readers to quickly and easily test different approaches on their desired models with minimal effort. Coupling this with the depth of examples and references provided, this text provides an excellent first graduate text on MCMC methods. … The book is certainly another fine addition on the literature on MCMC and should be used by anyone interested in gaining a solid foundation in MCMC methods and algorithms. …” —Gareth Peters (University of New South Wales), Statistics in Medicine, 2008 “… one of the most comprehensive and readable texts on stochastic simulation using the technique of Markov chain Monte Carlo. … this second edition has been extensively updated to include the recent literature. New sections on spatial modeling and model adequacy have now been included, together with more illustrative material. Many of the computer codes written in R and WinBUGS … are available for download from the web. This enhances the utility of the book, both as a reference for researchers and a text on modern Bayesian computation and Bayesian inference courses for students.” —C.M. O’Brien (CEFAS Lowestoft Laboratory, UK), ISI Short Book Reviews “…The book may be quite useful as a first book on MCMC. … The treatment is nontechnical, easily read, and may be a good starting point for a statistician with little or no prior knowledge of MCMC. There is also nonstandard material. I found the material on dynamical models (including non-Gaussian ones) particularly interesting. …” —Søren Feodor Nielsen (University of Copenhagen), Journal of Applied Statistics, Vol. 34, No. 7, December 2007 “…The book does have an impressive set of exercises … it would be appropriate for a course that wants to focus on using MCMC to solve applied Bayesian inference problems.” —Galin L. Jones, Mathematical Reviews, 2007j Praise for the First Edition: “…a must for every research library, and should be given serious consideration for use as a graduate text.” —ISI Short Book Reviews “…nicely focused, elementary-level coverage…makes this book a suitable choice for an introductory course.” —Journal of the ASA, March 2000 Table of ContentsIntroduction. Bayesian Inference. Approximate Methods of Inference. Markov Chains. MCMC. Gibbs Sampling. Metropolis-Hastings Algorithms. Further Topics in MCMC.
£111.89
Taylor & Francis Ltd Structural Equation Modeling with Mplus: Basic
Book SynopsisModeled after Barbara Byrne’s other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed illustrated and annotated testing of the hypothesized and post hoc models explanation and interpretation of all Mplus input and output files important caveats pertinent to the SEM application under study a description of the data and reference upon which the model was based the corresponding data and syntax files available under "Supplementary Material" below The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models.Intended for researchers, practitioners, and students who use SEM and Mplus, this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or Mplus, a prerequisite of basic statistics through regression analysis is recommended.Trade Review"Barbara Byrne has published another winner--a practically oriented, thorough, and accessible resource for students and researchers who want to harness the power of Mplus for their SEM analyses. The writing is clear and engaging. I anticipate assigning the book in my graduate SEM course and recommending it to fellow researchers. This book will be a valuable resource for moving from knowing about SEM to using it." - Rick H. Hoyle, Duke University, USA"This book provides a good starting point to newcomers to Mplus. It focuses, as it should for an introductory text, on the basics of 'classical' SEM. If you are new to SEM, plan on using Mplus, and are looking for an introductory text with minimal statistical jargon, this is it." - Albert Maydeu-Olivares, University of Barcelona, Spain"A solid introduction to the use of Mplus for SEM. All of the common types of structural equation models are illustrated using real examples, building the Mplus syntax from start to finish. The book is an excellent and readable guide for researchers and students who want to learn more about SEM in the context of Mplus." - Roger E. Millsap, Arizona State University, USA"A hallmark of Byrne's books is their accessibility to new users. … Byrne has done a great service to the field by bringing thousands of students and researchers to structural equation modeling through her clear writing and accessible examples. This book will be another contribution along those same lines. .... I field many, many questions … that could be answered by simply referring the asker to a book like Byrne's." - Kristopher J. Preacher, University of Kansas, USA"The book is targeted to non-mathematical readers, and hence it focuses on the applications of SEM. It does this very nicely, beginning from the part that covers the basic ideas of SEM and shows how to get started with the Mplus. Overall, this book is an excellent resource for a beginner interested in SEM with Mplus." -Kimmo Vehkalahti, Department of Social Research, Statistics, University of Helsinki, Finland"Through the use of illustrative examples, this much-needed and well-written book provides an accessible presentation of SEM with Mplus. Those new to SEM and/or Mplus will find Byrne’s book extremely useful as a companion textbook and long-term reference guide." - Sara J. Finney, James Madison University, USATable of ContentsPart 1: Introduction. 1. Structural Equation Models: The Basics. 2. Using the Mplus Program. Part 2: Single-Group Analyses. Confirmatory Factor Analytic Models 3. Testing the Factorial Validity of a Theoretical Construct (1st-order CFA Model). 4. Testing the Factorial Validity of Scores from a Measuring Instrument (1st-order CFA Model). 5. Testing the Validity of Scores from a Measuring Instrument (2nd-order CFA Model). The Full Latent Variable Model 6. Testing the Validity of a Causal Structure. Part 3: Multiple-Group Analyses. Confirmatory Factor Analytic Models 7. Testing for the Factorial Equivalence of a Measuring Instrument (Analysis of Covariance Structures). 8. Testing for the Equivalence of Latent Factor Means (Analysis of Mean and Covariance Structures). The Full Latent Variable Model 9. Testing for the Equivalence of a Causal Structure (Analysis of Covariance Structures). Part 4: Other Important Topics. 10. Testing Evidence of Construct Validity: The Multitrait-multimethod Model. 11. Testing Change Over Time: The Latent Growth Curve Model. 12. Testing Within- and Between-level Variability: The Multilevel Model.
£51.99
Taylor & Francis Ltd Statistical Power Analysis for the Social and
Book SynopsisThis is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal.Organized according to various techniques, chapters 1 – 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book‘s unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.Trade Review"This book extends earlier landmark texts by adding sample-size estimation for multilevel and longitudinal designs, meta-analysis, and structural-equation modeling. It is written thoughtfully and understandably. Readers will benefit enormously from the inclusion of computer code (in R, SAS and SPSS) for conducting the power analyses described. I recommend the book very highly to any researcher who wants to design research in the social sciences." - John B. Willett, Harvard University, USA"The author skillfully blends simple explanations of core concepts with more advanced material in a way that will make the work attractive to a range of readers in psychology and related disciplines. This text will be useful for postgraduate quantitative methods courses and for researchers. The coverage - from t tests through to multilevel models and SEM - is impressive. I found the examples of R, SPSS, and SAS code invaluable." - Thom Baguley, Nottingham Trent University, UK"This is a long-awaited, comprehensive book on power analysis after Cohen’s (1988) seminal book. The updated content accompanied by sample computer code is well suited for quantitative researchers in the social and behavioral sciences." - Wei Pan, Duke University, USA"This book provides a more comprehensive treatment of power analysis than any other work. ... This is likely to be the "go to" book for more complex designs. ... I found the writing style clear. ... The primary audience for this book would be all investigators who seek external funding for their work." - Warren W. Tryon, Fordham University, USA"This book would be good for departments of psychology, sociology, social work, nursing and public health. Most of the PhD programs in these departments have an advanced research methods course that could use this book. ... The Cohen book has been the standard in the field for over 20 years. ... This book would make a very nice update on a classic." - Jay Maddock, University of Hawaii, USA"This book is] a valuable extension beyond what is currently provided by other books on power. This book would contribute significantly to the field, most notably by covering the advanced and more complex techniques. …Liu and his work are well known in this field.…[This] book…could serve as the primary text for a …course on power. ...This book would basically have the field to itself." – Geoff Cumming, La Trobe University, AustraliaTable of Contents1. Introduction 2. Statistical Power 3. Power of Confidence Interval 4. Analysis of Variance 5. Linear Regression 6. Multivariate Analysis 7. Multi-level Models 8. Complex Multi-level Models 9. Meta-analysis 10. Structural Equation Models 11. Longitudinal Studies Appendix A. Cumulative Distribution Function for t, F, or x Appendix B. R Tutorial
£51.99
Cambridge University Press A Users Guide to Measure Theoretic Probability 8 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 8
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£44.64
Cambridge University Press Real Analysis and Probability 74 Cambridge Studies in Advanced Mathematics Series Number 74
Book SynopsisThis classic textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The first half of the book gives an exposition of real analysis: basic set theory, general topology, measure theory, integration, an introduction to functional analysis in Banach and Hilbert spaces, convex sets and functions and measure on topological spaces. The second half introduces probability based on measure theory, including laws of large numbers, ergodic theorems, the central limit theorem, conditional expectations and martingale's convergence. A chapter on stochastic processes introduces Brownian motion and the Brownian bridge. The edition has been made even more self-contained than before; it now includes a foundation of the real number system and the Stone-Weierstrass theorem on uniform approximation in algebras of functions. Several other sections have been revised and improved, and the comprehensive historical nTrade Review'A marvellous work which will soon become a standard text in the field for both teaching and reference … a complete and pedagogically perfect presentation of both the necessary preparatory material of real analysis and the proofs throughout the text. Some of the topics and proofs are rarely found in other textbooks.' Proceedings of the Edinburgh Mathematical Society'Careful, scholarly, and stimulating. It would be a pleasure to teach a mathematically-oriented graduate-level course from this text.' Short Book Reviews of the ISI'[It] will serve for a long time as a standard reference.' Zentralblatt fur und ihre Grenzgebiete'What makes the book special … is the care and scholarship with which the material is treated, and the choice of additional topics … not usually covered in first year graduate courses.' Mathematical Reviews'The book serves as a clear, rigorous, and complete introduction to modern probability theory using methods of mathematical analysis, and a description of relations between the two fields … it could be very useful for students interested in learning both topics, it can also serve as complementary reading to standard lectures. Teachers preparing their graduate level courses can use the book as an excellent, rigorously written and complete source.' EMS NewsletterTable of Contents1. Foundations: set theory; 2. General topology; 3. Measures; 4. Integration; 5. Lp spaces: introduction to functional analysis; 6. Convex sets and duality of normed spaces; 7. Measure, topology, and differentiation; 8. Introduction to probability theory; 9. Convergence of laws and central limit theorems; 10. Conditional expectations and martingales; 11. Convergence of laws on separable metric spaces; 12. Stochastic processes; 13. Measurability: Borel isomorphism and analytic sets; Appendixes: A. Axiomatic set theory; B. Complex numbers, vector spaces, and Taylor's theorem with remainder; C. The problem of measure; D. Rearranging sums of nonnegative terms; E. Pathologies of compact nonmetric spaces; Indices.
£54.14
Cambridge University Press Statistical Visions in Time
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£42.74
Cambridge University Press Ultrametric Calculus An Introduction to pAdic Analysis 4 Cambridge Studies in Advanced Mathematics Series Number 4
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£66.49
Cambridge University Press The Concept of Probability in Statistical Physics
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£34.19
Cambridge University Press The Statistical Consultant in Action
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£44.64
Cambridge University Press Probabilistic Causality
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£48.44
Cambridge University Press Biological Kinetics 12 Cambridge Studies in Mathematical Biology Series Number 12
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£40.84
Cambridge University Press Topics in the Constructive Theory of Countable Markov Chains
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£39.89
Cambridge University Press Mathematical Programs with Equilibrium Constraints
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£49.29
Cambridge University Press Epidemic Models Their Structure and Relation to Data 5 Publications of the Newton Institute Series Number 5
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£60.89
Cambridge University Press Factorization Calculus and Geometric Probability 33 Encyclopedia of Mathematics and its Applications Series Number 33
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£44.64
Cambridge University Press Gibbs States on Countable Sets 68 Cambridge Tracts in Mathematics Series Number 68
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£36.87
Cambridge University Press Introdction to Measure and Probability
Book SynopsisThe authors believe that a proper treatment of probability theory requires an adequate background in the theory of finite measures in general spaces. The first part of their book sets out this material in a form which not only provides an introduction for intending specialists in measure theory but also meets the needs of students of probability.Table of ContentsPreface; 1. Theory of sets; 2. Point set topology; 3. Set functions; 4. Construction and propertied of measures; 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; 10. What is probability?; 11. Random variables; 12. Characteristic functions; 13. Independence; 14. Finite collections of random variables; 15. Stochastic processes.
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Cambridge University Press Martingales and Stochastic Integrals
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£42.74
Cambridge University Press Topics in Applied Multivariate Analysis
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£39.89
Cambridge University Press Nonlinear Superposition Operators Cambridge Tracts in Mathematics 95 Cambridge Tracts in Mathematics Series Number 95
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£38.94
Cambridge University Press Contiguity of Probability Measures Some Applications in Statistics 63 Cambridge Tracts in Mathematics Series Number 63
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£41.79
Cambridge University Press Nonparametric Techniques in Statistical Inference
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Cambridge University Press Systems of Frequency Curves
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£39.89
Cambridge University Press Maximum Entropy and Bayesian Methods in Applied Statistics
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£35.14
Cambridge University Press Measurement Theory With Applications to Decisionmaking Utility and the Social Sciences 7 Encyclopedia of Mathematics and its Applications Series Number 7
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£69.17
Cambridge University Press Comparison of Statistical Experiments 36 Encyclopedia of Mathematics and its Applications Series Number 36
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£79.10
Cambridge University Press TimeSeries Analysis A Comprehensive Introduction for Social Scientists
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£45.59
Cambridge University Press Structural Equation Modeling Applications in Ecological and Evolutionary Biology
Book SynopsisStructural equation modelling (SEM) is a technique that is used to estimate, analyse and test models that specify relationships among variables. The ability to conduct such analyses is essential for many problems in ecology and evolutionary biology. This book begins by explaining the theory behind the statistical methodology, including chapters on conceptual issues, the implementation of an SEM study and the history of the development of SEM. The second section provides examples of analyses on biological data including multi-group models, means models, P-technique and time-series. The final section of the book deals with computer applications and contrasts three popular SEM software packages. Aimed specifically at biological researchers and graduate students, this book will serve as valuable resource for both learning and teaching the SEM methodology. Moreover, data sets and programs that are presented in the book can also be downloaded from a website to assist the learning process.Table of ContentsPart I. Theory: 1. Structural equation modelling: an introduction Scott L. Hershberger, George A. Marcoulides and Makeba M. Parramore; 2. Concepts of structural equation modelling in biological research Bruce H. Pugesek; 3. Modelling a complex conceptual theory of population change in the Shiras moose: history and recasting as a structural equation model Bruce H. Pugesek; 4. A short history of structural equation models Adrian Tomer; 5. Guidelines for the implementation and publication of structural equation models Adrian Tomer and Bruce H. Pugesek; Part II. Applications: 6. Modelling intra-individual variability and change in bio-behavioural developmental processes Patricia H. Hawley and Todd D. Little; 7. Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modelling James B. Grace; 8. From biological hypotheses to structural equation models: the imperfection of causal translation Bill Shipley; 9. Analysing dynamic systems: a comparison of structural equation modelling and system dynamics modelling Peter S. Hovmand; 10. Estimating analysis of variance models as structural equation models Michael J. Rovine and Peter C. M. Molenaar; 11. Comparing groups using structural equations James B. Grace; 12. Modelling means in latent variable models of natural selection Bruce H. Pugesek; 13. Modeling manifest variables in longitudinal designs - a two-stage approach Bret E. Fuller, Alexander von Eye; Philip K. Wood and Bobby D. Keeland; Part III. Computing: 14. A comparison of the SEM software packages Amos, EQS and LISREL Alexander von Eye and Bret E. Fuller; Index.
£48.44
Cambridge University Press Fixed Point Theory and Applications 141 Cambridge Tracts in Mathematics Series Number 141
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£42.74
Cambridge University Press Statistical Models
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£133.00
Cambridge University Press Asymptotic Efficiency of Nonparametric Tests
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£35.14
Cambridge University Press Statistical Calculation for Beginners
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£37.99
Cambridge University Press A Students Guide to Data and Error Analysis
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£66.50
Cambridge University Press Empirical Processes in MEstimation 06 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 6
Book SynopsisThe theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such areaTrade Review'… well written and provides a modern contribution to a very important class of nonparametric estimators.' N. D. C. Veraverbeke, Publication of the International Statistical Institute'… this excellent book will be extremely useful for graduate students and researchers in the general area of nonparametric estimation. It is a welcome addition to the existing literature and certainly recommended.' Niew Archief voor WiskundeTable of ContentsPreface; Reading guide; 1. Introduction; 2. Notations and definitions; 3. Uniform laws of large numbers; 4. First applications: consistency; 5. Increments of empirical processes; 6. Central limit theorems; 7. Rates of convergence for maximum likelihood estimators; 8. The non-i.i.d. case; 9. Rates of convergence for least squares estimators; 10. Penalties and sieves; 11. Some applications to semi-parametric models; 12. M-estimators; Appendix; References; Author index; Subject index; List of symbols.
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Cambridge University Press Tables of the Ordinates and Probability Integral of the Distribution of the Correlation Coefficient in Small Samples
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Cambridge University Press Control Theory and Dynamic Games in Economic Policy Analysis
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£42.74
Cambridge University Press Random Matrices High Dimensional Phenomena 367 London Mathematical Society Lecture Note Series Series Number 367
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£51.99
Cambridge University Press Stopping Times and Directed Processes 47 Encyclopedia of Mathematics and its Applications Series Number 47
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Cambridge University Press Pade Approximants 59 Encyclopedia of Mathematics and its Applications Series Number 59
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Cambridge University Press Regression Modeling with Actuarial and Financial Applications Author Edward W Frees Nov2009
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Cambridge University Press Exponential Random Graph Models for Social Networks Theory Methods and Applications 35 Structural Analysis in the Social Sciences Series Number 35
Book SynopsisExponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages and interpret the results.Trade Review'I've been waiting impatiently for this book and I was definitely not disappointed. Finally we have a sourcebook on ERGMs that is both comprehensive and comprehensible. Most of the chapters are written for quantitative researchers who are not statisticians. Many illustrative empirical applications are worked through. Software packages are discussed. For the researcher who is intrigued by the possibility of analyzing network data with an ERGM, or who is already trying to do so, this is an indispensable resource.' Peter Carrington, University of Waterloo'This collection offers readers an intuitive understanding of ERGMs, followed by a formal explanation of their statistical underpinnings as well as a methodological cookbook based on current software. Next, network scholars at the forefront of advancing theoretical and methodological contributions present eight compelling empirical studies. These studies illustrate how ERGMs offer exciting opportunities to advance theoretical understandings of network phenomena at the intra-organizational, inter-organizational, and societal levels.' Noshir Contractor, Jane S. and William J. White Professor of Behavioral Sciences, Northwestern University'p*, the exponential family of random graph distributions introduced by Frank and Strauss in 1986, has indeed become the best statistical model in network science. This edited volume is a must-have - Lusher, Koskinen, and Robins have put together a thorough compilation for both the p* novice and enthusiast. It is the handbook to own - and use!' Stanley Wasserman, Indiana UniversityTable of ContentsIntroduction Dean Lusher, Johan Koskinen and Garry Robins; 1. What are exponential random graph models Garry Robins and Dean Lusher; 2. The formation of social network structure Dean Lusher and Garry Robins; 3. A simplified account of ERGM as a statistical model Garry Robins and Dean Lusher; 4. An example of ERGM analysis Dean Lusher and Garry Robins; 5. Exponential random graph model fundamentals Johan Koskinene and Galina Daraganova; 6. Dependence graphs and sufficient statistics Johan Koskinen and Galina Daraganova; 7. Social selection, dyadic covariates and geospatial effects Garry Robins and Galina Daraganova; 8. Autologistic actor attribute models Galina Daraganova and Garry Robins; 9. ERGM extensions: models for multiple networks and bipartite networks Peng Wang; 10. Longitudinal models Tom Snijders and Johan Koskinen; 11. Simulation, estimation and goodness of fit Johan Koskinen and Tom Snijders; 12. Illustrations: simulation, estimation and goodness of fit Garry Robins and Dean Lusher; 13. Personal attitudes, perceived attitudes and social structures: a social selection model Dean Lusher and Garry Robins; 14. How to close a hole: exploring alternative closure mechanisms in inter-organizational networks Alessandro Lomi and Francesca Pallotti; 15. Interdependencies between working relations: multivariate ERGMs for advice and satisfaction Yu Zhao and Olaf Rank; 16. Brain, brawn or optimism? The structure and correlates of emergent military leadership Yuval Kalish and Gil Luria; 17. An ALAAM analysis of unemployment: the dual importance of who you know and where you live Galina Daraganova and Philippa Pattison; 18. Longitudinal changes in face-to-face and text message-mediated friendship networks Tasuku Igarashi; 19. The differential impact of directors' social and financial capital on corporate interlock formation Nicholas Harrigan and Matthew Bond; 20. Comparing networks: a structural correspondence between behavioural and recall networks Eric Quintane; 21. Modelling social networks: next steps Philippa Pattison and Tom Snijders.
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Cambridge University Press Stochastic Integration with Jumps 89 Encyclopedia of Mathematics and its Applications Series Number 89
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£70.82
Cambridge University Press The Essential Guide to Effect Sizes Statistical
Book SynopsisThis succinct and jargon-free introduction to effect sizes gives students and researchers the tools they need to interpret the practical significance of their results. Using a class-tested approach that includes numerous examples and step-by-step exercises, it introduces and explains three of the most important issues relating to the practical significance of research results: the reporting and interpretation of effect sizes (Part I), the analysis of statistical power (Part II), and the meta-analytic pooling of effect size estimates drawn from different studies (Part III). The book concludes with a handy list of recommendations for those actively engaged in or currently preparing research projects.Trade Review'Paul Ellis writes with a light touch, explains well, and uses numerous practical examples. He focuses on four of the issues that are central to the statistical changes now sweeping many disciplines - effect sizes, confidence intervals, power, and meta-analysis. This is a highly readable, highly practical book. It will be invaluable to anyone who wishes to contribute to - or even just understand - the research of the future.' Geoff Cumming, La Trobe University, Australia'Assessing the substantive significance of research is essential for both scientific progress and practical implications. This authoritative and well-written book gives relevant examples of key issues and offers practical guidelines for assessing the importance of research findings. The book concludes with clear recommendations for designing and carrying out good research and for assessing and reporting research findings.' William H. Starbuck, University of Oregon and Professor Emeritus, New York UniversityTable of ContentsList of figures; List of tables; List of boxes; Introduction; Part I. Effect Sizes and the Interpretation of Results: 1. Introduction to effect sizes; 2. Interpreting effects; Part II. The Analysis of Statistical Power: 3. Power analysis and the detection of effects; 4. The painful lessons of power research; Part III. Meta-Analysis: 5. Drawing conclusions using meta-analysis; 6. Minimizing bias in meta-analysis; Last word: thirty recommendations for researchers; Appendices: 1. Minimum sample sizes; 2. Alternative methods for meta-analysis; Bibliography; Index.
£36.09
Cambridge University Press Statistics for Anthropology
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£41.79
Cambridge University Press Bayesian Logical Data Analysis for the Physical Sciences
Book SynopsisIncreasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica notebooks are available.Trade Review'As well as the usual topics to be found in a text on Bayesian inference, chapters are included on frequentist inference (for contrast), non-linear model fitting, spectral analysis and Poisson sampling.' Zentralblatt MATH'The examples are well integrated with the text and are enlightening.' Contemporary Physics'The book can easily keep the readers amazed and attracted to its content throughout the read and make them want to return back to it recursively. It presents a perfect balance between theoretical inference and a practical know-how approach to Bayesian methods.' Stan Lipovetsky, TechnometricsTable of ContentsPreface; Acknowledgements; 1. Role of probability theory in science; 2. Probability theory as extended logic; 3. The how-to of Bayesian inference; 4. Assigning probabilities; 5. Frequentist statistical inference; 6. What is a statistic?; 7. Frequentist hypothesis testing; 8. Maximum entropy probabilities; 9. Bayesian inference (Gaussian errors); 10. Linear model fitting (Gaussian errors); 11. Nonlinear model fitting; 12. Markov Chain Monte Carlo; 13. Bayesian spectral analysis; 14. Bayesian inference (Poisson sampling); Appendix A. Singular value decomposition; Appendix B. Discrete Fourier transforms; Appendix C. Difference in two samples; Appendix D. Poisson ON/OFF details; Appendix E. Multivariate Gaussian from maximum entropy; References; Index.
£59.84
Cambridge University Press Several Complex Variables
Book SynopsisFirst published in 2000, this book provides a clear and complete picture of research in Several Complex Variables and its interactions with PDEs, algebraic geometry, number theory, and differential geometry. The expository nature of the articles makes this an excellent introduction for students as well as a basis for continuing research.Table of ContentsPreface; 1. Local holomorphic equivalence of real analytic submanifolds in CN M. Salah Baouendi and Linda Preiss Rothschild; 2. How to use cycle space in complex geometry Daniel Barlet; 3. Resolution of singularities Edward Bierstone and Pierre D. Milman; 4. Global regularity of the ∂-Neuman problem: a survey of the L2-Sobolev theory Harold P. Boas and Emial J. Straube; 5. Recent developments in the classification theory of compact Käehler manifolds Frederic Campana and Thomas Peternell; 6. Remarks on global irregularity in the ∂-Neumann problem Michael Christ; 7. Subelliptic estimates and finite type John P. D'Angelo and Joseph J. Kohn; 8. Pseudoconvex-concave duality and regularization of currents Jean-Pierre Demailly; 9. Complex dynamics in higher dimension John Erik Fornaess and Nessim Sibony; 10. Attractors in Ρ2 John Erik Fornaess and Brendan Weickert; 11. Analytic Hilbert quotients Peter Heinzner and Alan Huckleberry; 12. Varieties of minimal rational tangents on uniruled projective manifolds Jun-Muk Hwang and Ngaiming Mok; 13. Recent developments in Seiberg–Witten theory and complex geometry Christian Okonek and Andrei Teleman; 14. Recent techniques in hyperbolicity problems Yum-Tong Siu; 15. Rigidity theorems in Käehler geometry and fundamental groups of varieties Domingo Toledo; 16. Nevanlinna theory and diophantine approximation Paul Vojta.
£49.39
Cambridge University Press Probabilistic Reasoning in Multiagent Systems
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£42.74
Cambridge University Press The Principle of Relativity
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£35.14