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Simulation study of an estimator of bivariante survivor function and its variance estimator

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Simulation study of an estimator of bivariante survivor function and its variance estimator

Jin, Yu Lan (2008) Simulation study of an estimator of bivariante survivor function and its variance estimator. Masters thesis, Concordia University.

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Abstract

Bivariate survival data arises when we have either a pair of observation times for each individual or times on two related individuals, such as infection times for the two kidneys of a person or death times of twins. Such data are also often subject to censoring - bivariate censoring - i.e., exact observations may not be available on one or both of components because of drop-out or other reasons. Hence it is important to have an efficient, nonparametric bivariate survivor function estimator under censoring, i.e., a bivariate Kaplan-Meier estimator. In this thesis we carry out an extensive simulation study of an estimator proposed by Sen and Stute (2007), which involves solving for an eigenvector of a certain matrix. A comparison of the estimator with two other existing but unsatisfactory ones is also given using a small data-set. Moreover, variance of the former is computed using a bivariate analogue of Greenwood's formula, which involves solving a matrix equation of the form AXB=C

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (Masters)
Authors:Jin, Yu Lan
Pagination:ix, 66 leaves : ill. (some col.) ; 29 cm.
Institution:Concordia University
Degree Name:M. Sc.
Program:Mathematics
Date:2008
Thesis Supervisor(s):Sen, Arusharka
ID Code:975866
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:16
Last Modified:18 Jan 2018 17:41
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