LEADER 03952nam 22006615 450 001 9910254094903321 005 20250312221238.0 010 $a9783319281582 010 $a3319281585 024 7 $a10.1007/978-3-319-28158-2 035 $a(CKB)3710000000732118 035 $a(DE-He213)978-3-319-28158-2 035 $a(MiAaPQ)EBC4557236 035 $a(PPN)194375811 035 $a(EXLCZ)993710000000732118 100 $a20160614d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModeling Discrete Time-to-Event Data /$fby Gerhard Tutz, Matthias Schmid 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (X, 247 p. 58 illus., 3 illus. in color.) 225 1 $aSpringer Series in Statistics,$x2197-568X 311 08$a9783319281568 311 08$a3319281569 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- The Life Table -- Basic Regression Models -- Evaluation and Model Choice -- Nonparametric Modelling and Smooth Effects -- Tree-Based Approaches -- High-Dimensional Models - Structuring and Selection of Predictors -- Competing Risks Models -- Multiple-Spell Analysis -- Frailty Models and Heterogeneity -- Multiple-Spell Analysis -- List of Examples -- Bibliography -- Subject Index -- Author Index. 330 $aThis book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book. . 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aStatistics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aMathematical statistics$xData processing 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aStatistics and Computing 615 0$aStatistics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 0$aMathematical statistics$xData processing. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aStatistics and Computing. 676 $a003.83 700 $aTutz$b Gerhard$4aut$4http://id.loc.gov/vocabulary/relators/aut$089112 702 $aSchmid$b Matthias$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254094903321 996 $aModeling Discrete Time-to-Event Data$92129514 997 $aUNINA