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About this book
About this book
This book presents a collaborative approach to data analysis involving both scientists and statisticians that can be used to model unknown parameters using scientific information (apriori). Keeping the mathematics to a minimum, the text focuses on applications, statistical ideas, models and interpretations. It features WinBUGS throughout the computational problems and uses Monte Carlo methods for all simulations.
Prologue (Introductory Data Analysis).
Fundamental Ideas I. Integration vs. Simulation.
Fundamental Ideas II. One and Two Sample Problems.
Introduction to Regression.
Continuous Regression (Linear Models).
Diagnostic Testing: Binary Case.
Diagnostic Testing: Continuous Case.
Simulations (MCMC Theory).
Ronald Christensen is a Professor in the Department of Mathematics and Statistics at the University of New Mexico, Albuquerque. He is also a Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics as well as the former Chair of the ASA Section on Bayesian Statistical Science. Wesley Johnson is a Professor in the Department of Statistics at the University of California, Irvine. He is also a Fellow of the ASA and Chair-Elect of the ASA Section on Bayesian Statistical Science. Adam Branscum is an Associate Professor in the Department of Public Health at Oregon State University, Corvallis. Timothy E. Hanson is an Associate Professor in the Department of Statistics at the University of South Carolina, Columbia.
498 pages, 87 figs, 57 tabs
This is a very sound introductory text, and is certainly one which teachers of any course on Bayesian statistics beyond the briefest and most elementary should consider adopting. --David J. Hand, International Statistical Review (2011), 79 Unlike many Bayesian books which did not cover this topic extensively, this new book teaches readers how to illicit informative priors from field experts in great detail. ! Straightforward R codes are also provided for pinpointing hyperparameter values ! this book is particularly valuable in emphasizing the right approach to elicit prior, an important component of deriving posterior or predictive distribution. Another important feature of this new Bayesian textbook is its rich details. !The proofs never skip steps, and are easy to follow for readers taking only one or two semester math stat classes. The well-written text along with more than 70 figures and 50 plus tables add tremendously to the elucidation of the problems discussed in the book. Directly following some examples or important discussion in the text, readers can self-check whether they understand the materials by playing with some exercise problems, most of which are pretty straightforward. Christensen et al. provide many WinBUGS codes in the book and a website for readers to download these codes. In addition, the authors introduce how to perform Bayesian inferences using SAS codes on two occasions ! The book also recommends some other programs or websites that will facilitate computation ! This book is also characterized by its humor, ! [making] reading this Bayesian book more delightful. --Dunlei Cheng, Statistics in Medicine, 2011