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Modeling bivariate survival times by...

Qin, Rui.

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Modeling bivariate survival times by copulas.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling bivariate survival times by copulas./
作者:	Qin, Rui.
面頁冊數:	85 p.
附註:	Adviser: Michael P. Jones.
Contained By:	Dissertation Abstracts International67-08B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3229697
ISBN:	9780542833755

Modeling bivariate survival times by copulas.
Qin, Rui.

Modeling bivariate survival times by copulas. - 85 p.

Adviser: Michael P. Jones.

Thesis (Ph.D.)--The University of Iowa, 2006.

In multivariate survival analysis, incorporating and quantifying association among survival times in a statistical model poses a challenging problem. One promising approach is the application of copula function which models the dependence structure separately from the marginal distributions. The comprehensive family of Archimedean copulas provides flexibility in modeling different dependence structures.

ISBN: 9780542833755Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Modeling bivariate survival times by copulas.
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100 1 $a Qin, Rui. $3 1292919
245 1 0 $a Modeling bivariate survival times by copulas.
300 $a 85 p.
500 $a Adviser: Michael P. Jones.
500 $a Source: Dissertation Abstracts International, Volume: 67-08, Section: B, page: 4506.
502 $a Thesis (Ph.D.)--The University of Iowa, 2006.
520 $a In multivariate survival analysis, incorporating and quantifying association among survival times in a statistical model poses a challenging problem. One promising approach is the application of copula function which models the dependence structure separately from the marginal distributions. The comprehensive family of Archimedean copulas provides flexibility in modeling different dependence structures.
520 $a We consider the copula model with relative risk margins for bivariate survival time data. In essence, this serves as an extension of the classical marginal approach of using Cox regression to model the marginal survival functions by the addition of an association parameter from the copula. This parameter is treated as a measure of correlation between the bivariate survival times.
520 $a Estimation occurs in two stages. First, Cox regression is used to estimate the marginal survival functions. Second, a pseudo-likelihood for the association parameter is constructed by plugging in the marginal estimators and is then maximized over the association parameter. This two-stage estimation provides an alternative to the fully maximum likelihood procedure which is computation intensive due to the involvement of an infinite-dimensional cumulative baseline hazard parameter.
520 $a Empirical process theory is applied to establish consistency and asymptotic normality of the two-stage estimator. Simulation is used to study the behavior of the estimator and the asymptotic conclusions in small to medium size samples. Compared with the Clayton copula, the Frank copula model has advantages in speed of convergence and accommodation of negative association. Applications of the copula model to real data sets show satisfactory results for simultaneously modeling marginal effects and association.
590 $a School code: 0096.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 0 $a The University of Iowa. $3 1017439
773 0 $t Dissertation Abstracts International $g 67-08B.
790 $a 0096
790 1 0 $a Jones, Michael P., $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3229697