What does this research mean for the field?

Non-parametric estimators for mean inactivity time function can be effectively applied to both complete and censored data. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to provide effective nonparametric estimators for the mean inactivity time function, applicable to both complete and censored data.

March 7, 2026Open Access

Nonparametric Estimation of Quantile-Based Mean Inactivity Time Function

Key Points

The aim is to provide effective nonparametric estimators for the mean inactivity time function, applicable to both complete and censored data.
Developed nonparametric estimators for mean inactivity time function
Established asymptotic properties under regularity conditions
Conducted Monte Carlo simulation studies to evaluate estimator efficiency
Applied the methodology to three real data sets
Establishes efficiency of nonparametric estimators compared to other methods
Demonstrates practicality through real data application

Abstract

In this article, we propose non-parametric estimators for mean inactivity time function for complete and censored data. The asymptotic properties of the estimators are established using suitable regularity conditions. Monte Carlo simulation studies are used to study the efficiency of the estimators. Three real data sets are used to demonstrate the usefulness of the estimation procedure.

Nonparametric Estimation of Quantile-Based Mean Inactivity Time Function

Key Points

Abstract

Cite This Study