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Activity Number: 582 - Nonparametric Methods for Statistical Inference
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #304404 Presentation
Title: Maximum Approximate Bernstein Likelihood Estimation in Proportional Hazard Model for Interval-Censored Data
Author(s): Zhong Guan*
Companies: Indiana University South Bend
Keywords: Proportional Hazard Regression Model; Interval Censoring; Survival Curve; Density Estimation; Maximum Likelihood Estimation

Maximum approximate Bernstein likelihood estimates of density function and regression coefficients in the proportional hazard regression models based on interval-censored data are proposed and studied. A smooth estimate of the survival function with a bootstrap confidence interval is then obtained. Simulation study is conducted to show the finite sample performance of the proposed method. The proposed method is illustrated by real data applications.

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

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