This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an "extras" section. The main bod
"This book contains exercises and selected detailed solutions covering statistical theory (from basic probability theory through the theory of statistical inference) that is typically taught in course
Designed for a one-semester advanced undergraduate or graduate course, Statistical Theory: A Concise Introduction clearly explains the underlying ideas and principles of major statistical concepts, in
Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categori
Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analys
Based on the authors’ lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it co
Taking into account novel multivariate analyses as well as new options for many standard methods, this renamed, new edition shows readers how to perform multivariate statistical analyses and understan
"Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific question
Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeli
The advances of book history and editorial theory remind us that it is vital to look behind the text we read. Sukanta Chaudhuri explores, at a very fundamental level, how texts are constituted and ho
Based on the author’s more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the model
Based on a highly popular, well-established course taught by the authors, Stochastic Processes: An Introduction, Second Edition discusses the modeling and analysis of random experiments using the theo
Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to A
Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research mono
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined reg
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform,
The third edition of this introductory text on statistical ideas and methods in agriculture and experimental biology offers treatment of more advanced topics, with two new chapters on methods of multi
Sampling methods are integral to the design of surveys and experiments, to the validity of results, and thus to the study of statistics, social science, and a variety other disciplines that use statis
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bests
Evidence from randomized controlled clinical trials is widely accepted as the only sound basis for assessing the efficacy of new medical treatments. Statistical methods play a key role in all stages o
While 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 proble
Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuse
