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|Title:||On Logistic Regression Approach to Survival Data and Power Divergence Statistics for Life Tables|
|Doctoral Committee Chair(s):||Ying, Ziliang|
|Department / Program:||Statistics|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|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.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.
|Date Available in IDEALS:||2014-12-17|