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Title:On Logistic Regression Approach to Survival Data and Power Divergence Statistics for Life Tables
Author(s):Lai, Kunjung
Doctoral Committee Chair(s):Ying, Ziliang
Department / Program:Statistics
Discipline:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Statistics
Abstract:Efron (1988) proposes the use of standard logistic regression techniques to estimate hazard rates and survival curves from survival data. These techniques allow statisticians to use parametric regression modeling on survival data in a flexible way that provides both estimates and standard errors. In the first part of this thesis, large sample properties of this logistic regression method are developed. It is shown that under some regularity conditions the hazard rate and survival function estimators are consistent and their corresponding asymptotic normality results also hold. Extension of Efron's method to regression model is proposed and their asymptotic properties again are examined.
The second part of the thesis introduces two classes of power divergence statistics for life tables. The first class of statistics is similar to the power divergence family of Cressie and Read's (1984) for the analysis of contingency tables. The second type of statistics is easier to be interpreted geometrically than the first one. However, these two classes of statistics are asymptotically equivalent. A relatively complete large sample theory, including consistency and asymptotic normality, is provided.
Issue Date:1993
Type:Text
Description:89 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.
URI:http://hdl.handle.net/2142/72583
Other Identifier(s):(UMI)AAI9329089
Date Available in IDEALS:2014-12-17
Date Deposited:1993


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