MARC Record from Library of Congress

LEADER: 03075cam a2200301 a 4500
001 2010018875
003 DLC
005 20110909180951.0
008 100517s2011 flua b 001 0 eng
010 $a 2010018875
015 $aGBB007844$2bnb
016 7 $a015469239$2Uk
020 $a9781439803547 (hardcover : alk. paper)
020 $a1439803544 (hardcover : alk. paper)
035 $a(OCoLC)ocn262430643
040 $aDLC$cDLC$dYDX$dUKM$dBTCTA$dYDXCP$dBWX$dCDX$dUBY$dDLC
050 00 $aQA279.5$b.B3868 2011
082 00 $a519.5/42$222
245 00 $aBayesian ideas and data analysis :$ban introduction for scientists and statisticians /$cRonald Christensen ... [et al.].
260 $aBoca Raton, FL :$bCRC Press,$cc2011.
300 $axvii, 498 p. :$bill. ;$c27 cm.
490 1 $aChapman & Hall/CRC texts in statistical science series
504 $aIncludes bibliographical references and indexes.
505 0 $aPrologue -- Fundamental ideas I -- Integration versus simulation -- Fundamental ideas II -- Comparing populations -- Simulations -- Basic concepts of regression -- Binominal regression -- Linear regression -- Correlated data -- Count data -- Time to event data -- Time to event regression -- Binary diagnostic tests -- Nonparametric models -- Appendix A: Matrices and vectors -- Appendix B: Probability -- Appendix C: Getting started in R.
520 $a"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."--Publisher's description.
650 0 $aBayesian statistical decision theory.
700 1 $aChristensen, Ronald,$d1951-
830 0 $aTexts in statistical science.