Maximum Approximate Likelihood Estimation in Accelerated Failure Time Model for Interval-Censored Data

TL;DR

This paper introduces a new estimation method using Bernstein polynomial models for accelerated failure time models with interval-censored data, demonstrating improved accuracy and convergence in simulations and real data application.

Contribution

It develops a maximum approximate likelihood estimation approach employing Bernstein polynomial mixtures for AFT models with interval-censored data, enhancing estimation accuracy.

Findings

Proposed method outperforms competitors in simulations.

Convergence rates are established under certain conditions.

Effective application demonstrated on breast cosmetic data.

Abstract

The approximate Bernstein polynomial model, a mixture of beta distributions, is applied to obtain maximum likelihood estimates of the regression coefficients, and the baseline density and survival functions in an accelerated failure time model based on interval censored data including current status data. The rate of convergence of the proposed estimates are given under some conditions for uncensored and interval censored data. Simulation shows that the proposed method is better than its competitors. The proposed method is illustrated by fitting the Breast Cosmetic Data using the accelerated failure time model.