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Bayes'sche Analyse mit Stata von Thompson, John Taschenbuch/Softback Buch The

FREE US DELIVERY | ISBN: 1597181412 | Quality Books
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ISBN
1597181412
EAN
9781597181419
Date of Publication
2014-05-06
Release Title
Bayesian Analysis with Stata
Artist
Thompson, John
Brand
N/A
Colour
N/A
Book Title
Bayesian Analysis with Stata

Über dieses Produkt

Product Identifiers

Publisher
Statacorp LLC
ISBN-10
1597181412
ISBN-13
9781597181419
eBay Product ID (ePID)
202482850

Product Key Features

Number of Pages
302 Pages
Publication Name
Bayesian Analysis with Stata
Language
English
Subject
Probability & Statistics / General, Probability & Statistics / Bayesian Analysis
Publication Year
2014
Type
Textbook
Subject Area
Mathematics
Author
John Thompson
Format
Trade Paperback

Dimensions

Item Height
0.7 in
Item Weight
21.6 Oz
Item Length
9.3 in
Item Width
7.3 in

Additional Product Features

Intended Audience
College Audience
Reviews
"... the first comprehensive guide to employing Bayesian methods using Stata statistical software. ... until this book, there has been no unified presentation of how to implement Bayesian methods using Stata. ... A nice feature of the book is the use of real data ... I recommend it for Stata users who wish to employ Bayesian modeling within the Stata environment." -- International Statistical Review , 2015
Table Of Content
List of figures List of tables Preface Acknowledgments The problem of priors Case study 1: An early phase vaccine trial Bayesian calculations Benefits of a Bayesian analysis Selecting a good prior Starting points Exercises Evaluating the posterior Introduction Case study 1: The vaccine trial revisited Marginal and conditional distributions Case study 2: Blood pressure and age Case study 2: BP and age continued General log posteriors Adding distributions to logdensity Changing parameterization Starting points Exercises Metropolis-Hastings Introduction The MH algorithm in Stata The mhs commands Case study 3: Polyp counts Scaling the proposal distribution The mcmcrun command Multiparameter models Case study 3: Polyp counts continued Highly correlated parameters Case study 3: Polyp counts yet again Starting points Exercises Gibbs sampling Introduction Case study 4: A regression model for pain scores Conjugate priors Gibbs sampling with nonstandard distributions The gbs commands Case study 4 continued: Laplace regression Starting points Exercises Assessing convergence Introduction Detecting early drift Detecting too short a run Running multiple chains Convergence of functions of the parameters Case study 5: Beta-blocker trials Further reading Exercises Validating the Stata code and summarizing the results Introduction Case study 6: Ordinal regression Validating the software Numerical summaries Graphical summaries Further reading Exercises Bayesian analysis with Mata Introduction The basics of Mata Case study 6: Revisited Case study 7: Germination of broomrape Further reading Exercises Using WinBUGS for model fitting Introduction Installing the software Preparing a WinBUGS analysis Case study 8: Growth of sea cows Case study 9: Jawbone size Advanced features of WinBUGS GeoBUGS Programming a series of Bayesian analyses OpenBUGS under Linux Debugging WinBUGS Starting points Exercises Model checking Introduction Bayesian residual analysis The mcmccheck command Case study 10: Models for Salmonella assays Residual checking with Stata Residual checking with Mata Further reading Exercises Model selection Introduction Case study 11: Choosing a genetic model Calculating a BF Calculating the BFs for the NTD case study Robustness of the BF Model averaging Information criteria DIC for the genetic models Starting points Exercises Further case studies Introduction Case study 12: Modeling cancer incidence Case study 13: Creatinine clearance Case study 14: Microarray experiment Case study 15: Recurrent asthma attacks Exercises Writing Stata programs for specific Bayesian analysis Introduction The Bayesian lasso The Gibbs sampler The Mata code A Stata ado-file Testing the code Case study 16: Diabetes data Extensions to the Bayesian lasso program Exercises A Standard distributions References Author index Subject index
Synopsis
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability. The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research. The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
LC Classification Number
QA279.5.T56 2014

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