Estimating Survival Functions in the Case of Three or More Stochastically Ordered Populations
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Authors
Mei, Jeffrey
Issue Date
2016
Type
Thesis
Language
en_US
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Abstract
The time until the occurrence of an event of interest is a quantity that is often sought after by
clinicians, engineers, and numerous other scientists for understanding lifetimes. Through
scientific knowledge, it can be assumed that many distributions are stochastically ordered.
Including a stochastic ordering constraint on estimators can vastly improve the bias and
mean squared error properties of an estimator. Rojo (2004) developed a pair of estimators
for the case of estimating two stochastically ordered survival functions. The goal of this
research is to develop a generalization of Rojo’s estimators to accommodate for the case
of estimating more than two survival functions. The quality of the estimators developed
in this study is assessed through simulations, testing the estimators against a variety of
scenarios. The estimator proposed in this research is shown to have better mean squared
error properties than estimators proposed by Barmi and Mukerjee (2005) for cases with
equal censoring rates. However, for cases with unequal censoring between distributions,
Barmi and Mukerjee’s estimator performs better than the estimator proposed in this study.
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In Copyright