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 data. The authors cover a large number of statistical models, explore numerical approximations via MCMC simulation, and include numerous exercises and real-world examples. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. Code and other materials are available on the book's website.
Autorentext
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.
Klappentext
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 col
Inhalt
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.