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

## TL;DR

This paper introduces a new Bernstein likelihood estimation method for proportional hazard models with interval-censored data, providing smoother survival estimates and improved regression coefficient accuracy.

## Contribution

It proposes a maximum approximate Bernstein likelihood approach that enhances estimation of baseline density and regression coefficients in interval-censored survival analysis.

## Key findings

- Faster convergence rate for survival function estimates
- Better finite sample performance than existing methods
- Effective real data application demonstration

## Abstract

Maximum approximate Bernstein likelihood estimates of the baseline density function and the regression coefficients in the proportional hazard regression models based on interval-censored event time data are proposed. This results in not only a smooth estimate of the survival function which enjoys faster convergence rate but also improved estimates of the regression coefficients. Simulation shows that the finite sample performance of the proposed method is better than the existing ones. The proposed method is illustrated by real data applications.