Books in the Chapman & Hall/CRC Texts in Statistical Science series

Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.

Presents an introduction to the foundations and applications of Bayesian analysis. This book focuses on hierarchical Bayesian modeling as implemented through Markov chain Monte Carlo (MCMC) methods and related data analytic techniques.

This books is meant for a standard one-semester advanced undergraduate or graduate level course on Mathematical Statistics. It covers all the key topics - statistical models, linear normal models, exponential families, estimation, asymptotics of maximum likelihood, significance testing, and models for tables of counts.

This text presents key topics in mathematical statistics in a rigorous yet accessible manner. It covers aspects of probability, distribution theory, and random processes that are fundamental to a proper understanding of inference. The book also discusses the properties of estimators constructed from a random sample of ends, with sections on methods for estimating parameters in time series models and computationally intensive inferential techniques. The text challenges the more mathematically inclined students while providing an approachable explanation of advanced statistical concepts for students who struggle with existing texts.

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas.

Designed for a one-semester advanced undergraduate or graduate statistical theory course, this book clearly explains the underlying ideas, mathematics, and principles of major statistical concepts, including parameter estimation, confidence intervals, hypothesis testing, asymptotic analysis, Bayesian inference, linear models etc.

This book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by existing textbooks

This book is a first course in probability and statistics using R. The book assumes a mathematical background of Calculus II, though much of the book can be read with a much lower level of mathematics. The book incorporates R throughout all sections via simulations, data wrangling and/or data visualization.

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory.

It exposes students to the foundations of classical experimental design and observational studies through a modern framework. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions.

Presents the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. This book also presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective.

Rev. ed. of: Computer-aided multivariate analysis. 4th ed. c2004.

This book shows the elements of statistical science that are highly relevant for students who plan to become data scientists. However, most of the content focuses on the statistical methods and the theory behind them, rather than on data science.

This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results.

The book provides an introduction to functional data analysis (FDA), useful to students and researchers. FDA is now generally viewed as a fundamental subfield of statistics. FDA methods have been applied to science, business and engineering.

Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies.

The book introduces Bayesian networks using simple yet meaningful examples. Discrete Bayesian networks are described first followed by Gaussian Bayesian networks and mixed networks. All steps in learning are illustrated with R code.

This text develops students' professional skills in statistics with applications in finance. It bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The authors explai

This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unba

Building on the author's more than 35 years of teaching experience, Modeling and Analysis of Stochastic Systems, Third Edition, covers the most important classes of stochastic processes used in the modeling of diverse systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.