Statistical Theory & Methods

This monograph contains many ideas on the analysis of survival data to present a comprehensive account of the field. The value of survival analysis is not confined to medical statistics, where the benefit of the analysis of data on such factors as life expectancy and duration of periods of freedom ..

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..

By gathering many results scattered throughout the literature, this book offers a convenient, systematic way to learn about antedependence models and the important statistical inference procedures associated with these models. It presents both informal methods of inference, such as graphical methods..

Applied Bayesian Forecasting and Time Series Analysis..

Emphasizing applications and practical computing aspects, this book presents an up-to-date review of Bayesian hierarchical methods. It provides a complete treatment of the main modeling settings, including meta-analysis, time series, nonlinear regression, and multilevel, multivariate, panel, spatial..

This book provides detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, the authors introduce variou..

Designed to make theory work for students, this clearly written, easy-to-understand work serves as the ideal texts for courses Regression, Experimental Design, and Linear Models in a broad range of disciplines. Moreover, applied statisticians will find the book a useful reference for the general app..

This book should be of interest to senior undergraduate and postgraduate students of applied statistics...

Applied Statistics - Principles and Examples..

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice. ..

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, the third edition provides an up-to-date, cohesive ..

Emphasizing the use of WinBUGS and R to analyze real data, this text presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing d..