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Activity Number: 644
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #309965
Title: Shape-Constrained Nonparametric Maximum Likelihood Estimation for Interval-Censored Data
Author(s): Clifford Anderson-Bergman*+
Companies: University of California, Irvine
Keywords: Interval Censored ; nonparametric ; Shape Constrained ; Log Concave ; Survival

Interval censoring occurs when time to event is only known up to an interval, rather than exact time to event. When analyzing interval censored data, a non-parametric estimator is often desired, in part due to difficulties in assessing model fits. Much work has been put into the computation of the non-parametric maximum likelihood estimator (NPMLE). However, the estimates for values of interest of the survival function, such as the quartiles, have very large standard errors due to the jagged form of the estimator. By forcing the estimator to be constrained to the class of log concave functions, we insure a once differentiable survival estimator which has much better operating characteristics than the unconstrained NPMLE without the need to specify any smoothing parameters. Using a novel algorithm capable of computing the log NPMLE for moderate sized data sets, we examine the performance of the log concave NPMLE. We also examine how the estimator behaves under the violation of the log concave restriction and investigate potential goodness of fit tests.

Authors who are presenting talks have a * after their name.

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