Inference on Survival Reliability with Type-I Censored Weibull data

TL;DR

This paper develops an exact inference method for reliability analysis using Weibull and other lifetime distributions, improving accuracy over existing approximations especially with censored data and small samples.

Contribution

It introduces a new approach for exact parametric tests and confidence intervals tailored for censored lifetime data, enhancing reliability analysis accuracy.

Findings

The new method outperforms existing approaches with censored and complete data.

Simulation results confirm improved accuracy and robustness.

Numerical examples demonstrate practical applicability in reliability engineering.

Abstract

Reliability inference based on parametric distributions is an important problem in electrical and mechanical engineering. Most existing methods rely on approximations or bootstrap procedures, which may not perform satisfactorily when data are censored or sample sizes are small. Hence, there is an urgent need to develop exact inference approaches for these situations. This article introduces a new approach for deriving exact parametric tests and confidence intervals for distributions such as the lognormal, loglogistic, and Weibull. We revisit several issues in classical reliability analysis based on the survival function. Because lifetime data are often censored in practice, the proposed approach is designed for such settings. We illustrate the method using the Weibull distribution and expect it to be applicable to other widely used lifetime distributions such as the loglogistic distribution. Our Simulation study show that the new approach provides better performance than existing methods when handling complete data and type-I censored data. Two numerical examples are provided to demonstrate the application of the proposed method. The proposed method is expected to be widely applicable in reliability engineering and survival analysis, offering a robust alternative to existing methods, particularly in scenarios involving censored data and small sample sizes.