Bayesian estimation of heteroscedastic skew-normal error regression model

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Oloyede Isiaka; Abiodun Alfred A.

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Journal Paper

Paper Information

Journal: JOURNAL OF STATISTICAL MODELLING: THEORY AND APPLICATIONS Year:2024 | Volume:5 | Issue:2 Page(s): 153-163

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Information Journal Paper

Title

Bayesian estimation of heteroscedastic skew-normal error regression model

Author(s)

Oloyede Isiaka | Abiodun Alfred A. | Issue Writer Certificate

Keywords

Bayesian

‎Heteroscedasticity

‎Modeling and least squares

‎Skew-normal‎

‎Simulation

Abstract

In statistics‎, ‎errors are inherent in data and models‎, ‎particularly heteroscedasticity and skew-normal error structures‎. ‎These errors were simultaneously generated and infused into the data‎, ‎leading to uncertainty in parameter estimation‎. ‎The statistician uses statistical knowledge to elicit information and guide decision-making‎. ‎Both classical and Bayesian restricted Stein-rule least squares were compared when the data were contaminated with the aforementioned errors‎. ‎This study proposed an innovative Bayesian generalized restricted Stein-rule least squares method with heteroscedastic skew-normal errors‎, ‎which was ultimately found to be more efficient compared to non-Bayesian restricted Stein-rule least square estimators‎. ‎The study observed excellent performance of the Bayesian frameworks‎, ‎including the Bayes estimate and posterior mean‎, ‎in comparison to the classical restricted Stein-rule least squares estimators‎. ‎Therefore‎, ‎the study recommends Bayesian generalized restricted Stein-rule least squares to analysts and researchers who may encounter such errors in their data‎.

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