Classical and Bayesian Inference for the Burr Type XII Distribution Under Generalized Progressive Type I Hybrid Censored Sample

DOI

10.2991/jsta.d.201211.001How to use a DOI?

Keywords

Burr Type XII distribution; Confidence intervals; EM algorithm; Generalized progressive hybrid censoring; Markov chain Monte Carlo technique

Abstract

This paper describes the classical and Bayesian estimation for the parameters of the Burr Type XII distribution based on generalized progressive Type I hybrid censored sample. We first discuss the maximum likelihood estimators of unknown parameters using the expectation-maximization (EM) algorithm and associated interval estimates using Fisher information matrix. We then derive the Bayes estimators with respect to different symmetric and asymmetric loss functions. In this regard, we use Lindley's approximation and importance sampling methods. Highest posterior density (HPD) intervals of unknown parameters are constructed as well. The results of simulation studies and real data analysis are conducted to compare the performance of the proposed point and interval estimators.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Parya Parviz
AU  - Hanieh Panahi
PY  - 2020
DA  - 2020/12/18
TI  - Classical and Bayesian Inference for the Burr Type XII Distribution Under Generalized Progressive Type I Hybrid Censored Sample
JO  - Journal of Statistical Theory and Applications
SP  - 547
EP  - 557
VL  - 19
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.201211.001
DO  - 10.2991/jsta.d.201211.001
ID  - Parviz2020
ER  -