Preference of Prior for Two-Component Mixture of Lomax Distribution

Authors

Faryal Younis¹, Muhammad Aslam², M. Ishaq Bhatti^{3, *,}

¹School of Applied Sciences and Humanities, National University of Technology, Islamabad, Pakistan

²Department of Mathematics and Statistics, Riphah International University, Islamabad, Pakistan

³La Trobe University, Melbourne, Australia

^*Corresponding author. Email: i.bhatti@latrobe.edu.au

DOI

10.2991/jsta.d.210616.002How to use a DOI?

Keywords

Mixture of Lomax distribution; Censored sampling; Elicitation of hyperparameter; Bayes estimator; Posterior risk; Loss function

Abstract

Recently, El-Sherpieny et al., (2020), suggested Type-II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regard being given to the choice of priors and posterior risk associated with the model. This paper fills this gap and derived the new LD model with minimum posterior risk for the selection of priors. It derives a closed form expression for Bayes estimates and posterior risks using square error loss function (SELF), weighted loss function (WLF), quadratic loss function (QLF) and DeGroot loss function (DLF). Prior predictive approach is used to elicit the hyperparameters of mixture model. Analysis of Bayes estimates and posterior risks is presented in terms of sample size , mixing proportion and censoring rate , with the help of simulation study. Usefulness of the model is demonstrated on applying it to simulated and real-life data which show promising results in terms of better estimation and risk reduction.

Copyright

© 2021 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  - Faryal Younis
AU  - Muhammad Aslam
AU  - M. Ishaq Bhatti
PY  - 2021
DA  - 2021/06/24
TI  - Preference of Prior for Two-Component Mixture of Lomax Distribution
JO  - Journal of Statistical Theory and Applications
SP  - 407
EP  - 424
VL  - 20
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.210616.002
DO  - 10.2991/jsta.d.210616.002
ID  - Younis2021
ER  -