| Summary: | Change-point detection in hazard rate function is an important research topic in survival
analysis. In this dissertation, we firstly review existing methods for single change-point detection in
piecewise exponential hazard model. Then we consider the problem of estimating the change point in
the presence of right censoring and long-term survivors while using Kaplan-Meier estimator for the
susceptible proportion. The maximum likelihood estimators are shown to be consistent. Taking one
step further, we propose an counting process based and least squares based change-point detection
algorithm. For single change-point case, consistency results are obtained. We then consider the
detection of multiple change-points in the presence of long-term survivors via maximum likelihood
based and counting process based method. Last but not least, we use a weighted least squares based
and counting process based method for detection of multiple change-points with long-term survivors
and covariates. For multiple change-points detection, simulation studies show good performances of
our estimators under various parameters settings for both methods. All methods are applied to real
data analyses. === Includes bibliography. === Dissertation (Ph.D.)--Florida Atlantic University, 2014. === FAU Electronic Theses and Dissertations Collection
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