A Weibull COMP-Bessel distribution to analyse life testing data

Abstract In this paper, we deal with a problem in which the items are arranged in a series system and the sample size is unknown, giving rise to a proportional hazard model. More specifically, we propose a model where the baseline distribution is Weibull, and the distribution of the sample size is COMP-Bessel resulting in a Weibull COMP-Bessel (WCOMPB) distribution. The maximum likelihood estimators of the parameters are studied and their performance is examined by extensive simulation studies. The penalised maximum likelihood estimation procedure is used to resolve computational challenges due to the complexity of the generalization. A score test is presented to compare its performance to its sub-model. To illustrate the significance of the proposed model, three real datasets are examined and it is shown that WCOMPB fits better than several existing models.

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Publication Details

Journal
Journal of Statistical Theory and Practice
Published
2025-10-28
DOI
https://doi.org/10.1007/s42519-025-00508-5
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
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article

A Weibull COMP-Bessel distribution to analyse life testing data

Choung Min Ng, Ramesh C. Gupta, S. H. Ong, Zheng Wei
Journal of Statistical Theory and Practice
Statistical Distribution Estimation and Applications
article

A Weibull COMP-Bessel distribution to analyse life testing data

Choung Min Ng, Ramesh C. Gupta, S. H. Ong, Zheng Wei
article en

Abstract

Abstract In this paper, we deal with a problem in which the items are arranged in a series system and the sample size is unknown, giving rise to a proportional hazard model. More specifically, we propose a model where the baseline distribution is Weibull, and the distribution of the sample size is COMP-Bessel resulting in a Weibull COMP-Bessel (WCOMPB) distribution. The maximum likelihood estimators of the parameters are studied and their performance is examined by extensive simulation studies. The penalised maximum likelihood estimation procedure is used to resolve computational challenges due to the complexity of the generalization. A score test is presented to compare its performance to its sub-model. To illustrate the significance of the proposed model, three real datasets are examined and it is shown that WCOMPB fits better than several existing models.

Journal of Statistical Theory and PracticeVol. 19(4)
University of Malaya (MY), UCSI University (MY), University of Maine (US), Texas A&M University – Corpus Christi (US)
Openalex Percentile: Top 71%
Statistical Distribution Estimation and Applications
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