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000755936 020__ $$a9783319281582$$q(electronic book)
000755936 020__ $$a3319281585$$q(electronic book)
000755936 020__ $$z9783319281568
000755936 035__ $$aSP(OCoLC)ocn951809501
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000755936 1001_ $$aTutz, Gerhard,$$eauthor.
000755936 24510 $$aModeling discrete time-to-event data$$h[electronic resource] /$$cGerhard Tutz, Matthias Schmid.
000755936 264_1 $$aSwitzerland :$$bSpringer,$$c2016.
000755936 300__ $$a1 online resource (x, 247 pages) :$$billustrations.
000755936 336__ $$atext$$btxt$$2rdacontent
000755936 337__ $$acomputer$$bc$$2rdamedia
000755936 338__ $$aonline resource$$bcr$$2rdacarrier
000755936 4901_ $$aSpringer series in statistics,$$x0172-7397
000755936 504__ $$aIncludes bibliographical references and indexes.
000755936 5050_ $$aIntroduction -- The Life Table -- Basic Regression Models -- Evaluation and Model Choice -- Nonparametric Modelling and Smooth Effects -- Tree-Based Approaches -- High-Dimensional Models -- Structuring and Selection of Predictors -- Competing Risks Models -- Multiple-Spell Analysis -- Frailty Models and Heterogeneity -- Multiple-Spell Analysis -- List of Examples -- Bibliography -- Subject Index -- Author Index.
000755936 506__ $$aAccess limited to authorized users.
000755936 520__ $$aThis book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book. .
000755936 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 22, 2016).
000755936 650_0 $$aDiscrete-time systems$$xMathematical models.
000755936 7001_ $$aSchmid, Matthias,$$eauthor.
000755936 77608 $$iPrint version:$$aTutz, Gerhard.$$tModeling discrete time-to-event data.$$d[Cham], Switzerland : Springer, c2016$$z9783319281568$$w(DLC)  2016942538
000755936 830_0 $$aSpringer series in statistics.
000755936 852__ $$bebk
000755936 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-28158-2$$zOnline Access$$91397441.1
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