Bayesian Ideas and Data Analysis

An Introduction for Scientists and Statisticians
1. Auflage 2010
ISBN: 978-1-4398-0354-7
Verlag: CRC Press

Buch, Englisch, 516 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1064 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

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.

Zielgruppe

Advanced undergraduate and graduate students in statistics, biostatistics, epidemiology, and science; statisticians; professionals in science and engineering.

Autoren/Hrsg.

Christensen, Ronald

Johnson, Wesley

Branscum, Adam

Hanson, Timothy E

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Prologue. Fundamental Ideas I. Integration versus Simulation. Fundamental Ideas II. Comparing Populations. Simulations. Basic Concepts of Regression. Binomial Regression. Linear Regression. Correlated Data. Count Data. Time to Event Data. Time to Event Regression. Binary Diagnostic Tests. Nonparametric Models. Appendices. References.

Über Autor(innen)

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.