Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)

Christensen, Ronald

ISBN 10: 1439803544 / ISBN 13: 9781439803547

Published by CRC Press, 2010

About this Item

Emphasizing the use of WinBUGS and R to analyze real data, thsi book 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 data. Series: Chapman & Hall/CRC Texts in Statistical Science. Num Pages: 516 pages, 87 black & white illustrations, 57 black & white tables. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 261 x 187 x 31. Weight in Grams: 1130. . 2010. 1st Edition. hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781439803547

Bibliographic Details

• Title: Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)
• Publisher: CRC Press
• Publication Date: 2010
• Language: English
• ISBN 10: 1439803544
• ISBN 13: 9781439803547
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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 questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data.

The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions.

The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data.

Data sets and codes are provided on a supplemental website.

About the Author

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.