大葉大學圖書館 |

Modeling discrete time-to-event data[electronic resource] /

纪录类型:	书目-电子资源 : Monograph/item
[NT 15000414] null:	003.83
[NT 47271] Title/Author:	Modeling discrete time-to-event data/ by Gerhard Tutz, Matthias Schmid.
作者:	Tutz, Gerhard.
[NT 51406] other author:	Schmid, Matthias.
出版者:	Cham : : Springer International Publishing :, 2016.
面页册数:	x, 247 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Discrete-time systems - Mathematical models.
标题:	Statistics.
标题:	Statistical Theory and Methods.
标题:	Statistics for Life Sciences, Medicine, Health Sciences.
标题:	Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
标题:	Statistics and Computing/Statistics Programs.
ISBN:	9783319281582
ISBN:	9783319281568
[NT 15000228] null:	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.
[NT 15000229] null:	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.
电子资源:	http://dx.doi.org/10.1007/978-3-319-28158-2

Modeling discrete time-to-event data[electronic resource] /
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:

486979
Discrete-time systems
--Mathematical models.

LC Class. No.: QA402

Dewey Class. No.: 003.83

Modeling discrete time-to-event data[electronic resource] /
LDR:02644nmm a2200325 a 4500 001 458878
003 DE-He213
005 20161129141001.0
006 m d
007 cr nn 008maaau
008 170113s2016 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 660404
245 1 0 $a Modeling discrete time-to-event data $h [electronic resource] / $c by Gerhard Tutz, Matthias Schmid.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
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 486979
650 1 4 $a Statistics. $3 145349
650 2 4 $a Statistical Theory and Methods. $3 464135
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 464109
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 464573
650 2 4 $a Statistics and Computing/Statistics Programs. $3 463725
700 1 $a Schmid, Matthias. $3 660405
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 511017
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-28158-2
950 $a Mathematics and Statistics (Springer-11649)