Open AccessStatistical Inference of Chen Distribution Based on Type I Progressive Hybrid Censored Samples
Author(s) -
Tanmay Kayal,
Yogesh Mani Tripathi,
Debasis Kundu,
Manoj Kumar Rastogi
Publication year - 2022
Publication title -
statistics, optimization and information computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.297
H-Index - 12
eISSN - 2311-004X
pISSN - 2310-5070
DOI - 10.19139/soic-2310-5070-486
Subject(s) - estimator , fisher information , mathematics , bayes' theorem , statistics , chen , inference , statistical inference , type (biology) , data set , algorithm , computer science , bayesian probability , artificial intelligence , paleontology , ecology , biology
In this paper we study the problem of estimating unknown parameters of a two-parameter distribution with bathtub shape under the assumption that data are type I progressive hybrid censored. We derive maximum likelihood estimators and then obtain the observed Fisher information matrix. Bayes estimators are also obtained under the squared error loss function and highest posterior density intervals are constructed as well. We perform a simulation study to compare proposed methods and analyzed a real data set for illustration purposes. Finally we establish optimal plans with respect to cost constraints and obtain comments based on a numerical study.