Preference of Prior for Two-Component Mixture of Lomax Distribution
- https://doi.org/10.2991/jsta.d.210616.002How to use a DOI?
- Mixture of Lomax distribution, Censored sampling, Elicitation of hyperparameter, Bayes estimator, Posterior risk, Loss function
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/jsta.d.210616.002 ID - Younis2021 ER -