Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm

Authors:
Mimi Zhang;Qingpei Hu;Min Xie;Dan Yu
Affiliations:
-;-;-;-
Venue:
Computational Statistics & Data Analysis
Year:
2014

Citing 9
Cited 0

Modified moment estimation for the two-parameter Birnbaum--Saunders distribution

Computational Statistics & Data Analysis
Lifetime analysis based on the generalized Birnbaum-Saunders distribution

Computational Statistics & Data Analysis
An R implementation for generalized Birnbaum-Saunders distributions

Computational Statistics & Data Analysis
A multiple imputation approach to the analysis of interval-censored failure time data with the additive hazards model

Computational Statistics & Data Analysis
Estimation of extreme percentiles in Birnbaum-Saunders distributions

Computational Statistics & Data Analysis
Recursive partitioning on incomplete data using surrogate decisions and multiple imputation

Computational Statistics & Data Analysis
A method for increasing the robustness of multiple imputation

Computational Statistics & Data Analysis
Generalized interval estimation for the Birnbaum-Saunders distribution

Computational Statistics & Data Analysis
Shape and change point analyses of the Birnbaum-Saunders-t hazard rate and associated estimation

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

The aim of this paper is to propose an approach to constructing lower confidence limits for a reliability function and investigate the effect of a sampling scheme on the performance of the proposed approach. This is accomplished by using a data-completion algorithm and certain Monte Carlo methods. The data-completion algorithm fills in censored observations with pseudo-complete data while the Monte Carlo methods simulate observations for complicated pivotal quantities. The Birnbaum-Saunders distribution, the lognormal distribution and the Weibull distribution are employed for illustrative purpose. The results of three cases of data-analysis are presented to validate the applicability and effectiveness of the proposed methods. The first case is illustrated through simulated data, and the last two cases are illustrated through two real-data sets.