Probability and statistics Books
Taylor & Francis Ltd Sequential Analysis
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£43.69
Taylor & Francis Ltd Risk Measures and Insurance Solvency Benchmarks
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£114.00
Taylor & Francis Ltd Structural Equation Modeling for Health and
Book SynopsisStructural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across many disciplines, addressing issues unique to health and medicine. SEM is often used in practice to model and test hypothesized causal relationships among observed and latent (unobserved) variables, including in analysis across time and groups. It can be viewed as the merging of a conceptual model, path diagram, confirmatory factor analysis, and path analysis. In this textbook the authors also discuss techniques, such as mixture modeling, that expand the capacity of SEM using a combination of both continuous and categorical latent variables. Features: Basic, intermediate, and advanced SEM topics Detailed applications, parTable of ContentsPart I Introduction to Concepts and Principles of Structural Equation Modeling for Health and Medical Research 1. Introduction and Brief History of Structural Equation Modeling for Health and Medical Research2. Vocabulary, Concepts and Usages of Structural Equation Modeling Part II Theory of Structural Equation Modeling 3 The Form of Structural Equation Models4 Model Estimation and Evaluation5 Model Identifiability and Equivalence Part III Applications and Examples of Structural Equation Modeling for Health and Medical Research 6 Choosing Among Competing Specifications 7 Measurement Models for Patient-Reported Outcomes and Other Health-related Outcomes8 Exploratory Factor Analysis9 Mediation and Moderation10 Measurement Bias, Multiple Indicator Multiple Cause Modeling and Multiple Group Modeling 11 Latent Class Analysis 12 Latent Profile Analysis13 Structural Equation Modeling with Longitudinal Data14 Growth Mixture Modeling 15 Special Topics
£43.99
Taylor & Francis Ltd Statistics and Data Visualisation with Python
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£114.00
Taylor & Francis Ltd PostShrinkage Strategies in Statistical and Machine Learning for High Dimensional Data
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£118.75
Taylor & Francis Ltd Fuzzy TOPSIS
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£147.25
Taylor & Francis Ltd Nanohertz Gravitational Wave Astronomy
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£56.04
Taylor & Francis Ltd HumanintheLoop
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£45.99
Taylor & Francis Ltd Testing R Code Chapman HallCRC the R
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£45.99
Taylor & Francis Ltd Reliability Analysis with Minitab
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£45.99
Taylor & Francis Ltd Artificial Intelligence and Causal Inference
Book SynopsisArtificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis mTrade Review" Both deep learning and causal inference are fast-moving fields, and the author covers the latest topics and methods well. The book has a high ratio of equations to text, and even more technical material is contained in appendices at the end of each chapter."Stanley E. Lazic, University of Ottawa, Series A: Statisics in Society, 2022."The book is suitable for use in a graduate-level course on AI. The exercises are challenging but their answers are provided in the end of the book. Not all contents are understandable by the statistics community or commonly useful in the practice of statistics. I enjoyed reading this book. I recommend this book to engineering, data science, predictive business, statistics and computing professionals."Ramalingam Shanmugam, School of Health Administration, Texas State University, San Marcos, Texas, Journal of Statistical Computation and Simulation, 2023.Table of Contents1. Deep Neural Networks. 2. Deep Wide Neural Networks. 3. Dynamics of Output of Neural Networks. 4. Deep Generative Models. 5. Representation Learning. 5. Graph Representation Learning. 6. Deep Learning for Causal Inference. 7. Deep Learning for Counterfactual Inference and Treatment Estimation. 8. Reinforcement Learning, Meta-Learning for Causal Inference and Quantum Causal Analysis.
£104.50
Taylor & Francis Ltd RealWorld Evidence in a PatientCentric Digital
Book SynopsisReal-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance, real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in a Patient-Centric Digital Era provides perspectives, examples, and insights on the innovative application of real-world evidence to meet patient needs and improve healthcare, with a focus on the pharmaceutical industry.This book presents an overview of key analytical issues and best practices. Special attention is paid to the development, methodologies, and other salient features of the statistical and data science techniques that are customarily used to generate real-world evidence. It provides a review of key topics and emerging trends in cutting-edge data science and health innovation.Features: Provides an overview Table of ContentsPreface: Real World Evidence and Digital Innovation to Combat Noncommunicable Diseases. 1. Real World Evidence Generation. 2. Applications of RWE for Regulatory Uses. 3. Ethics & Bioethics. 4. Real- World Data, Big Data and Artificial Intelligence: Recent Development and Emerging Trends in the European Union. 5. Patient centricity and Precision Medicine. 6. Health Information Technology. 7. Digital Health Technologies and Innovations. 8. Economic Analysis and Outcome Assessment. 9. Partnerships and Collaborations. 10. Global Perspective: China Big Data Collaboration to Improve Patient Care. 11. The Future of Patient-Centric Data-Driven Healthcare
£99.75
Taylor & Francis Ltd Handbook of Mathematics and Statistics for the Environment
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£73.14
Taylor & Francis Ltd Statistics Behind the Headlines
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£25.99
Taylor & Francis Ltd Statistics Behind the Headlines
Book SynopsisHow do you learn about what's going on in the world? Did a news headline grab your attention? Did a news story report on recent research? What do you need to know to be a critical consumer of the news you read? If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information.Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story. The chapters are organized aTrade Review"We live in a time when information channels have been de-centralized, and the usual gatekeepers —journalists, experts, public officials— have lost much of their power to control what information we receive. There are positive consequences to this cultural shift, but also negative ones: misinformation, disinformation, and simple misunderstandings due to ignorance run rampant. This book is an antidote to that. Using a friendly and occasionally humorous voice, John Bailer and Rosemary Pennington teach us, general readers, how to become more critical consumers of the statistics we see every day in the news and in social media. What a delight."- Alberto Cairo, University of Miami"With each chapter, the authors cover not only various aspects of real life but also offer various flavors of modern science, like the notion of data literacy, an overview of organizations responsible for data collection, reporting practices, and many more. They take readers on a path to investigate the background research and the forefront of storytelling. They motivate readers to hone their’ skills as data-savvy consumers of daily news. [...] Science searches for broad patterns that capture general truths about the world. Narratives search for connections through human experience that assign meaning and value to reality. The book by Bailer and Pennington masterfully stitched these two worlds together, helping to approach both research results and news coverage with a critical eye. [...] I am sure that this book is a great educational resource for those interested in many fields: statistics, journalism, ethics, education, and their interplay and practical applications."- Elena N. Naumova in Journal of Public Health Policy, January 2024"A beautifully accessible, thought-provoking book that should be an invaluable resource for journalists who use statistics - which, these days, is all journalists. Through clear, current examples, the authors rigorously interrogate the way data are interpreted and presented, and force us to question how to do this in a way that better serves the public without feeding misinformation, hype, or hysteria. I'm sure I will be using it as a reference guide regularly, and recommending it to other reporters."- Angela Saini, Science Journalist, New York"An excellent and really useful aspect of the book is that it’s jointly written by a statistician and a journalist. As the book itself makes clear, journalists and statisticians have different skills, different motivations, and different reasons for reporting their work. And there are others involved – usually the headline on a media story is not written by the journalist who wrote the study, for example. If you’re aiming to make sense of a media story about statistics, and the story behind the story, you need to understand how all that works. Not only can Bailer and Pennington cover both sides – they both already have long experience of working across that professional divide, and understanding of how and why statisticians and journalists do what they do. The book is primarily aimed at a general audience – but I think that it will also be helpful to statisticians and journalists who need to understand more about what the ‘other side’ does. They would be able to skip some of the detail about their own profession, but seeing how everything fits together is important to anyone who needs to communicate effectively about numbers. There are other strengths too. Each main chapter uses the same basic structure for making sense of a statistically-based story, and readers can use the same structure as a basis for picking apart stories for themselves. And there are links to the excellent Stats + Stories podcast series, developed by Bailer, Pennington and others, that take the ideas forward."- Kevin McConway, The Open University"Statistics Behind the Headlines makes statistics accessible for wide audiences. The book teaches foundational statistical concepts through engaging and timely stories. It would make an excellent textbook for any course that teaches statistical thinking, whether for undergraduates, journalists, or medical professionals. Each chapter allows readers to "peer under the hood" of data-based claims and equips them with the tools needed to critically evaluate these claims. The reader-friendly style provides a refreshing contrast to the stodgy and intimidating style of most statistics books. Readers will develop an appreciation for statistics; learn how to spot hype and misinformation; and become more savvy consumers of numbers-based news."- Kristin Sainani, Stanford University Table of Contents1. A Field Guide to Reading the Statistics behind the Headlines 2. Predicting Global Population Growth and Framing how you Report It 3. Social Media and Mental Health 4. Speedy Sneakers: Technological Boosterism or Sound Science? 5. Investigating Series Binge-watching 6. Tracking the Spread of "False News" 7. Modeling What It Means to "Flatten the Curve" 8. One Governor, Two Outcomes, and Three COVID Tests 9. Research Reproducibility and Reporting Results 10. Now, what?
£58.89
Taylor & Francis Ltd Applied Bayesian Forecasting and Time Series Analysis Chapman HallCRC Texts in Statistical Science
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£166.25
Taylor & Francis Ltd Analysis of Messy Data Volume II Nonreplicated Experiments Nonreplicated Experiments v 2
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£147.25
Taylor & Francis Ltd Introduction to Multivariate Analysis
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£92.14
Taylor & Francis Ltd Point Processes
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£166.25
Taylor & Francis Ltd Analysis of Binary Data
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£156.75
Taylor & Francis Ltd Analysis of Infectious Disease Data
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£147.25
Taylor & Francis Ltd Artificial Intelligence Frontiers in Statistics Al and Statistics III
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£168.12
Taylor & Francis Ltd Statistical Analysis of Reliability Data 27 Chapman HallCRC Texts in Statistical Science
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£114.00
Taylor & Francis Ltd Statistical Inference Based on the likelihood Based on the Likeihood 68 Chapman HallCRC Monographs on Statistics and Applied Probability
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£114.00
Taylor & Francis Ltd Mixed Poisson Processes 77 Chapman HallCRC Monographs on Statistics and Applied Probability
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£147.25
Taylor & Francis Ltd Multiple Comparisons Theory And Methods Guilford School Practitioner
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£147.25
Taylor & Francis Using Statistics to Understand the Environment Routledge Introductions to Environment Environmental Science
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£199.50
Taylor & Francis Ltd Using Statistics to Understand the Environment Routledge Introductions to the Environment
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£37.99
Taylor & Francis Statistics for Sport and Exercise Studies
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£156.75
Taylor & Francis Ltd An Objective Theory Of Probability Routledge Revivals
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£51.29
Taylor & Francis Mathematics for Economists with Applications
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£175.75
Taylor & Francis Mathematics for Economists with Applications
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£99.75
Taylor & Francis Ltd A Primer of Multivariate Statistics
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£51.29
Taylor & Francis Ltd Latent Variable and Latent Structure Models
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£51.29
Taylor & Francis Ltd Modeling Intraindividual Variability With Repeated Measures Data
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£49.39
Taylor & Francis Ltd New Developments and Techniques in Structural Equation Modeling
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£41.59
Taylor & Francis MetaRegression Analysis in Economics and Business
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£142.50
Taylor & Francis The Meaning of the Concept of Probability in Application to Finite Sequences Routledge Revivals
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£45.59
Taylor & Francis The Meaning of the Concept of Probability in Application to Finite Sequences Routledge Revivals
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£32.99
Taylor & Francis Statistics for Business
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£175.75
Taylor & Francis Longitudinal Data Analysis
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£128.25
Taylor & Francis Longitudinal Data Analysis
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£51.29
Taylor & Francis Ltd Introductory Regression Analysis with Computer Application for Business and Economics
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£99.75
Taylor & Francis Introductory Regression Analysis with Computer Application for Business and Economics
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£73.14
Elsevier Science Essential Bayesian Models
Book SynopsisFocuses on key methodologies and applications for Bayesian models and computation. This book describes parametric and nonparametric Bayesian methods for modeling, and how to use modern computational methods to summarize inferences using simulation.Table of Contents1. Model Selection and Hypothesis Testing based on Objective Probabilities and Bayes Factors; 2. Bayesian Model Checking and Model Diagnostics; 3. Bayesian Nonparametric Modeling and Data Analysis: An Introduction; 4. Some Bayesian Nonparametric Models; 5. Bayesian Modeling in the Wavelet Domain; 6. Bayesian Methods for Function Estimation; 7. MCMC Methods to Estimate Bayesian Parametric Models; 8. Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities; 9. Bayesian Modelling and Inference on Mixtures of Distributions; 10. Variable Selection and Covariance Selection in Multivariate Regression Models; 11. Dynamic Models; 12. Elliptical Measurement Error Models – A Bayesian Approach; 13. Bayesian Sensitivity Analysis in Skew-elliptical Models; 14. Bayesian Methods for DNA Microarray Data Analysis; 15. Bayesian Biostatistics; 16. Innovative Bayesian Methods for Biostatistics and Epidemiology; 17. Modeling and Analysis for Categorical Response Data; 18. Bayesian Methods and Simulation-Based Computation for Contingency Tables; 19. Teaching Bayesian Thought to Nonstatisticians
£53.99
Taylor & Francis Ltd The Doctrine of Chances A Method of Calculating the Probabilities of Events in Play Library of Science Classics
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£128.25
Taylor & Francis Applied Statistics for Public Policy
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£128.25
Taylor & Francis Advanced Structural Equation Modeling
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£128.25