Bayesian Exploration of 4-Component Rayleigh Mixture Model with Wind Speed Data Application

Arifa Jahangir, Farzana Noor, Maryam Siddiqa, Mehwish Zaman, Rabia Asghar and António Pereira

Mathematical Problems in Engineering, 2022, vol. 2022, 1-16

Abstract: This article focuses on the application of wind speed data of 4 coastal areas of Baluchistan, that is, Gawadar, Jiwani, Ormara, and Pasni on 4-Component Rayleigh Mixture Model (4-CRMM) under Bayesian context. Type I right censoring scheme is used because it is popular in reliability theory and survival analysis. To accomplish the objective, the Bayes estimates (BEs) of the parameter of the mixture model along with their posterior risks (PRs) using informative prior (IP) and noninformative prior (NIP) are obtained. Hyperparameters are obtained by employing the prior predictive method. BEs are calculated under two distinct loss functions, squared error loss function (SELF) and modified squared error loss function (MSELF). The statistical properties and performance of the BEs under said loss functions are also evaluated by simulation study for different sample sizes.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8929859

DOI: 10.1155/2022/8929859

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