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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Error function Burr XII distribution : Quantile regression, classical and Bayesian applications

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pp. 1139–1168Vol. 27Issue 6September 2024DOI: 10.47974/JSMS-1216XML
Received:
08 Mar 2023
Published Online:
19 Sep 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1216
Pages:
1139–1168

Abstract

Statistical distributions form the bedrock of many parametric analyses. Thus, identifying an apt model is key in parametric statistical inference. This study formulates the error function Burr XII (EFBXII) model. The EFBXII model is parsimonious compared to other extended forms of the Burr XII model. The EFBXII model density and hazard functions display desirable shapes. The bivariate appendage of the EFBXII model was formulated for studying bivariate data. Maximum likelihood estimators of the EFBXII model parameters were proposed and simulations carried out to assess their behaviours. The simulation results revealed the estimators are well behaved. A quantile regression model is contrived by re-parametrizing the EFBXII model. Utility of the EFBXII model and its corresponding regression are illustrated utilizing data. Empirical findings in both cases reveal that the developed models offer better fit compared to some existing distributions for the given data sets. Bayesian illustrations were carried out and in all cases the estimates are quite close to those from the classical method.

Keywords

Error functionBurr XIIQuantile regressionBayesianBivariate

Subject Classifications

(2010) 60E0562F1062E15

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