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Nonparametric Maximum Likelihood Estimation

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The Statistical Analysis of Interval-censored Failure Time Data

Part of the book series: Statistics for Biology and Health ((SBH))

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Abstract

Estimation of a survival function is perhaps the first and most commonly required task in the analysis of failure time data. There can be many reasons or purposes for such a task. For example, an estimated survival function can be used to assess the validity of an assumption about a particular parametric model for the underlying survival variable of interest. Also, one may need to estimate survival functions to estimate certain survival probabilities, to graphically compare several different treatments, or to predict survival probabilities for future patients. In the case where a parametric model can be reasonably assumed for the underlying survival function, the estimation problem is relatively easy, and the maximum likelihood approach discussed in Section 2.3 is commonly used for the problem. In this chapter, attention is focused on nonparametric estimation of survival functions along with estimation of hazard functions.

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© 2006 Springer Science+Business Media, Inc.

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(2006). Nonparametric Maximum Likelihood Estimation. In: The Statistical Analysis of Interval-censored Failure Time Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-37119-2_3

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