The majority of data sets collected by researchers in all disciplines are multivariate. This book comprehensively covers a variety of multivariate analysis techniques using R. It provides extensive examples of R code used to apply the multivariate techniques.
There has been a dramatic growth in the development and application of Bayesian inferential methods. This book introduces Bayesian modeling by the use of computation using the R language. The new edition contains changes in the R code illustrations.
Business Analytics for Managers helps readers extract knowledge and actionable insight from real business data. The text emphasizes data-driven thinking and provides a quick-start guide to one of the most powerful software solutions available.
This book explains hazard-based analyses of competing risks and multistate data using the R statistical programming code, placing special emphasis on interpretation of results. Includes real data examples, and encourages readers to simulate their own data.
This text introduces general state space models in detail before focusing on dynamic linear models, emphasizing their Bayesian analysis. It illustrates all the fundamental steps needed to use dynamic linear models in practice, using R.
Classical statistical theory is mainly the creation of two men: Ronald A. Fisher and Jerzy Neyman. This book explores the relationship between them, their interactions with other influential statisticians and the statistical history they helped create.
Generalized estimating equations have become increasingly popular in biometrical, econometrical and psychometrical applications. In this book, they are derived in a unified way using pseudo maximum likelihood estimation and the generalized method of moments.
The theory of inequalities has applications in virtually every branch of mathematics. This revised and expanded edition of a classic work on inequalities will be of interest to statisticians, probabilists, and mathematicians.
This updated textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models.
This accessible book provides a versatile treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It contains many worked out examples and exercises.