Maximum Approximate Bernstein Likelihood Estimation in Proportional Hazard Model for Interval-Censored Data

06/20/2019

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by   Zhong Guan, et al.

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Maximum approximate Bernstein likelihood estimates of density function and regression coefficients in the proportional hazard regression models based on interval-censored event time data are proposed and studied. Smooth estimates of the survival and density functions are then obtained. The estimate of the regression coefficients can also be improved. Simulation study is conducted to show the finite sample performance of the proposed method. The proposed method is illustrated by real data applications.