Distance-Based Estimation Under Progressive Type-I Interval Censoring

Abstract

Monte Carlo simulation is used to demonstrate improved estimation performance of proposed distance-type estimators for lifetime models under progressive Type-I interval censoring. We propose novel distance-based estimators for lifetime models under progressive Type-I interval censoring. These estimators minimize the discrepancy between observed and model-based conditional failure probabilities using either quadratic or Mahalanobis distances, providing natural alternatives to maximum likelihood estimators (MLEs). Through extensive Monte Carlo simulations, we demonstrate that the Mahalanobis estimator outperforms MLE, particularly under heavy censoring or sparse data. The quadratic estimator also yields competitive results, especially under model misspecification. Two real data examples illustrate the practical advantages of the proposed approach.