Confidence intervals for the current status model

TL;DR

This paper introduces a bootstrap-based method for constructing pointwise confidence intervals for the distribution function in the current status model, utilizing the smoothed maximum likelihood estimator and comparing it with existing approaches.

Contribution

The paper proposes a novel bootstrap approach for confidence intervals in the current status model using SMLE, and evaluates its performance against traditional methods.

Findings

Bootstrap-based confidence intervals perform well in simulations.

The proposed method outperforms existing approaches in certain scenarios.

Real data applications demonstrate practical utility.

Abstract

We discuss a new way of constructing pointwise confidence intervals for the distribution function in the current status model. The confidence intervals are based on the smoothed maximum likelihood estimator (SMLE) and constructed using bootstrap methods. Other methods to construct confidence intervals, using the non-standard limit distribution of the (restricted) MLE, are compared to our approach via simulations and real data applications.