MARC Record from marc_columbia

LEADER: 04409cam a2200733Ii 4500
001 15120624
005 20220604234026.0
006 m o d
007 cr mn|||||||||
008 170713t20172017flua ob 001 0 eng d
035 $a(OCoLC)ocn993587602
035 $a(NNC)15120624
040 $aYDX$beng$erda$epn$cYDX$dEBLCP$dOCLCO$dN$T$dCRCPR$dYDX$dOCLCO$dN$T$dOCLCQ$dOSU$dOCLCQ$dUAB$dMERER$dOCLCQ$dERL$dOCLCQ$dNRC$dU3W$dMERUC$dOCLCQ$dWYU$dTYFRS$dOCLCQ$dUKAHL$dOCLCQ$dZCU$dOCLCO$dVHC$dOCLCO
019 $a1007405842$a1066470506$a1303518101
020 $a9781351811569$q(electronic bk.)
020 $a1351811568$q(electronic bk.)
020 $a9781351811552$q(electronic bk.)
020 $a135181155X$q(electronic bk.)
020 $a9781315211060$q(e-book master)
020 $a1315211068$q(e-book master)
020 $z9781138627826$q(hardcover ;$qalk. paper)
020 $z1138627828$q(hardcover ;$qalk. paper)
020 $z9781351811552$q(e-pub)
020 $z9781351811545$q(mobi/kindle)
020 $z1351811541$q(mobi/kindle)
024 7 $a10.1201/9781315211060$2doi
035 $a(OCoLC)993587602$z(OCoLC)1007405842$z(OCoLC)1066470506$z(OCoLC)1303518101
050 4 $aQA279$b.L4295 2017
072 7 $aMAT$x003000$2bisacsh
072 7 $aMAT$x029000$2bisacsh
072 7 $aMAT$x017000$2bisacsh
082 14 $a519.5$bL482
049 $aZCUA
100 1 $aLee, Youngjo,$eauthor.
245 10 $aData analysis using hierarchical generalized linear models with R /$cYoungjo Lee, Lars Rönnegård, Maengseok Noh.
264 1 $aBoca Raton :$bCRC Press, Taylor & Francis Group,$c[2017]
264 4 $c©2017
300 $a1 online resource (xii, 321 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
520 $aSince their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
504 $aIncludes bibliographical references and indexes.
505 0 $aIntroduction -- GLMs via iterative weighted least squares -- Inference for models with unobservables -- HGLMs: from method to algorithm -- HGLMs modeling in R -- Double HGLMs -- using the dhglm package -- Fitting multivariate HGLMs -- Survival analysis -- Joint models -- Further topics.
588 0 $aPrint version record.
650 0 $aLinear models (Statistics)$vTextbooks.
650 0 $aMultilevel models (Statistics)$vTextbooks.
650 0 $aR (Computer program language)
650 6 $aR (Langage de programmation)
650 7 $aMATHEMATICS$xApplied.$2bisacsh
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aMATHEMATICS$xLinear & Nonlinear Programming.$2bisacsh
650 7 $aLinear models (Statistics)$2fast$0(OCoLC)fst00999084
650 7 $aMultilevel models (Statistics)$2fast$0(OCoLC)fst01028902
650 7 $aR (Computer program language)$2fast$0(OCoLC)fst01086207
655 0 $aElectronic book.
655 4 $aElectronic books.
655 7 $aTextbooks.$2fast$0(OCoLC)fst01423863
655 7 $aTextbooks.$2lcgft
700 1 $aRönnegård, Lars,$d1970-$eauthor.
700 1 $aNoh, Maengseok,$eauthor.
776 08 $iPrint version:$aLee, Youngjo.$tData analysis using hierarchical generalized linear models with R.$dBoca Raton : CRC Press, Taylor & Francis Group, [2017]$z9781138627826$w(DLC) 2017003194$w(OCoLC)971020918
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15120624$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS