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LEADER: 06106cam 2200913 a 4500
001 ocn823839429
003 OCoLC
005 20220811211435.0
008 130110s2013 nyu ob 001 0 eng d
006 m o d
007 cr cnu---unuuu
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020 $a9781441909251$q(electronic bk.)
020 $a1441909257$q(electronic bk.)
020 $a1441909249
020 $a9781441909244
020 $z9781441909244
024 7 $a10.1007/978-1-4419-0925-1$2doi
035 $a(OCoLC)823839429$z(OCoLC)1005782299$z(OCoLC)1026440641$z(OCoLC)1069613966$z(OCoLC)1110855118$z(OCoLC)1112578073$z(OCoLC)1162731186$z(OCoLC)1198079710$z(OCoLC)1204070393$z(OCoLC)1259061307
037 $bSpringer
050 4 $aQA278.2$b.W35 2013
060 4 $aOnline Book
072 7 $aQA$2lcco
072 7 $aPBT$2bicssc
072 7 $aMAT029000$2bisacsh
082 04 $a519.5/36$223
100 1 $aWakefield, Jon.
245 10 $aBayesian and frequentist regression methods /$cJon Wakefield.
264 1 $aNew York, NY :$bSpringer,$c©2013.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file
347 $bPDF
490 1 $aSpringer series in statistics,$x0172-7397
505 00 $tInferential Approaches --$tFrequentist Inference --$tBayesian Inference --$tHypothesis Testing and Variable Selection --$tIndependent Data --$tLinear Models --$tGeneral Regression Models --$tBinary Data Models --$tDependent Data --$tLinear Models --$tGeneral Regression Models --$tNonparametric Modeling --$tPreliminaries for Nonparametric Regression --$tSpline and Kernel Methods --$tNonparametric Regression with Multiple Predictors --$gAppendices --$tDifferentiation of Matrix Expressions --$tMatrix Results --$tSome Linear Algebra --$tProbability Distributions and Generating Functions --$tFunctions of Normal Random Variables --$tSome Results from Classical Statistics --$tBasic Large Sample Theory.
504 $aIncludes bibliographical references and index.
520 $aBayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place. The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book.
546 $aEnglish.
650 0 $aRegression analysis$xMathematical models.
650 0 $aBayesian statistical decision theory.
650 0 $aRegression analysis.
650 2 $aRegression Analysis
650 2 $aBayes Theorem
650 6 $aThéorie de la décision bayésienne.
650 6 $aAnalyse de régression.
650 6 $aThéorème de Bayes.
650 7 $aRegression analysis.$2fast$0(OCoLC)fst01432090
650 7 $aBayesian statistical decision theory.$2fast$0(OCoLC)fst00829019
650 7 $aRegression analysis$xMathematical models.$2fast$0(OCoLC)fst01093277
653 4 $aStatistics.
653 4 $aMathematical statistics.
653 4 $aStatistical Theory and Methods.
653 00 $astatistiek
653 00 $astatistische analyse
653 00 $astatistical analysis
653 10 $aStatistics (General)
653 10 $aStatistiek (algemeen)
655 4 $aElectronic books.
776 08 $iPrinted edition:$z9781441909244
830 0 $aSpringer series in statistics.
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856 40 $3Scholars Portal$uhttp://books.scholarsportal.info/viewdoc.html?id=/ebooks/ebooks3/springer/2013-08-13/4/9781441909251
856 40 $3SpringerLink$uhttps://doi.org/10.1007/978-1-4419-0925-1
856 40 $3SpringerLink$uhttps://link.springer.com/book/10.1007%2F978-1-4419-0924-4
856 40 $3SpringerLink$uhttps://link.springer.com/book/10.1007%2F978-1-4419-0925-1
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948 $hNO HOLDINGS IN IME - 324 OTHER HOLDINGS