國立虎尾科技大學 |

Modeling discrete time-to-event data

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling discrete time-to-event data/ by Gerhard Tutz, Matthias Schmid.
作者:	Tutz, Gerhard.
其他作者:	Schmid, Matthias.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	x, 247 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Discrete-time systems - Mathematical models. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-28158-2
ISBN:	9783319281582

Modeling discrete time-to-event data
Tutz, Gerhard.

Modeling discrete time-to-event data[electronic resource] /by Gerhard Tutz, Matthias Schmid. - Cham :Springer International Publishing :2016. - x, 247 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

Introduction -- 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.

This 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.

ISBN: 9783319281582

Standard No.: 10.1007/978-3-319-28158-2doiSubjects--Topical Terms:

857031
Discrete-time systems
--Mathematical models.

LC Class. No.: QA402

Dewey Class. No.: 003.83

Modeling discrete time-to-event data
LDR:02644nam a2200325 a 4500 001 865218
003 DE-He213
005 20161129141001.0
006 m d
007 cr nn 008maaau
008 170720s2016 gw s 0 eng d
020 $a 9783319281582 $q (electronic bk.)
020 $a 9783319281568 $q (paper)
024 7 $a 10.1007/978-3-319-28158-2 $2 doi
035 $a 978-3-319-28158-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 003.83 $2 23
090 $a QA402 $b .T967 2016
100 1 $a Tutz, Gerhard. $3 1110623
245 1 0 $a Modeling discrete time-to-event data $h [electronic resource] / $c by Gerhard Tutz, Matthias Schmid.
260 $a Cham : $c 2016. $b Springer International Publishing : $b Imprint: Springer,
300 $a x, 247 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 0172-7397
505 0 $a Introduction -- 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.
520 $a This 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.
650 0 $a Discrete-time systems $x Mathematical models. $3 857031
650 1 4 $a Statistics. $3 556824
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 670129
650 2 4 $a Statistics and Computing/Statistics Programs. $3 669775
700 1 $a Schmid, Matthias. $3 1110624
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 882180
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-28158-2
950 $a Mathematics and Statistics (Springer-11649)