Statistical Inference for Lognormal Distribution with Type-I Progressive Hybrid Censored Data. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Sen, Tanmay; Singh, Sukhdev; Tripathi, Yogesh Mani
• Is Part Of: American journal of mathematical and management sciences. Volume 38:Issue 1(2019)
• Page Start: 70
• Page End: 95
• Publication Date: 2019-01-02
• Subjects: EM algorithm -- MH algorithm -- prediction -- HPD interval -- equal-tail interval -- optimal censoring; Operations research -- Periodicals; Management science -- Periodicals; Periodicals; 658.4034
• DOI: 10.1080/01966324.2018.1484826
• Languages: English
• ISSNs: 0196-6324

Abstract

SYNOPTIC ABSTRACT: This article deals with problems of estimation and prediction under classical and Bayesian approaches when lifetime data following a lognormal distribution are observed under type-I progressive hybrid censoring. We first obtain maximum likelihood estimates, Bayes estimates, and corresponding interval estimates of unknown lognormal parameters. We then develop predictors to predict censored observations and construct prediction intervals. Further, we analyze two real data sets and conduct a simulation study to compare the performance of proposed methods of estimation and prediction. Finally, optimal censoring schemes are constructed under cost constraints and a conclusion is presented.