Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

Junnumtuam, Sunisa; Niwitpong, Sa-Aat; Niwitpong, Suparat

doi:10.32604/cmes.2022.022098

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The strength of RWM lies in its local exploration capability, allowing it to meticulously probe the parameter space using a symmetric proposal density, which simplifies the acceptance criteria. This simplicity facilitates easier tuning and implementation, often requiring only the adjustment of the proposal distribution's scale to balance the acceptance rate and the chain's mixing [26,27]. Concurrently, credible intervals for unknown parameters are generated during this procedure.…”

Section: Random Walk Metropolis Algorithmmentioning

confidence: 99%

Bayesian Inference for the Gamma Zero-Truncated Poisson Distribution with an Application to Real Data

Srisuradetchai,

Niyomdecha

2024

Symmetry

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This article presents Bayesian estimation methods applied to the gamma zero-truncated Poisson (GZTP) and the complementary gamma zero-truncated Poisson (CGZTP) distributions, encompassing both one-parameter and two-parameter models. These distributions are notably flexible and useful for modeling lifetime data. In the one-parameter model case, the Jeffreys prior is mathematically derived. The use of informative and noninformative priors, combined with the random walk Metropolis algorithm within a Bayesian framework, generates samples from the posterior distributions. Bayesian estimators’ effectiveness is examined through extensive simulation studies, in comparison with the maximum likelihood method. Results indicate that Bayesian estimators provide more precise parameter estimates, even with smaller sample sizes. Furthermore, the study and comparison of the coverage probabilities (CPs) and average lengths (ALs) of the credible intervals with those from Wald intervals suggest that Bayesian credible intervals typically yield shorter ALs and higher CPs, thereby demonstrating the effectiveness of Bayesian inference in the context of GZTP and CGZTP distributions. Lastly, Bayesian inference is applied to real data.

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Section: Random Walk Metropolis Algorithmmentioning

confidence: 99%

Bayesian Inference for the Gamma Zero-Truncated Poisson Distribution with an Application to Real Data

Srisuradetchai,

Niyomdecha

2024

Symmetry

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“…The sixth paper "Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data" by Junnumtuam et al [6] created a brand-new discrete three-parameter distribution known as the zero-inflated cosine geometric distribution, and apply the resulting count model to a COVID-19 data set.…”

mentioning

confidence: 99%

Introduction to the Special Issue on New Trends in Statistical Computing and Data Science

Chesneau¹,

Doosti²

2023

Computer Modeling in Engineering &Amp; Sciences

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The interface between computer science and statistics has developed considerably in recent years, with exponential progress in the fields of data analysis, stochastic modeling, machine learning, econometrics, simulation, algorithms, classification, and networks. Innovative discoveries in this field appear every day, opening new scientific horizons for the modern world. This is especially true in the post-2020 period, with the treatment of large volumes of data that feed the daily operations of large corporations, as well as the development of artificial intelligence, including advanced machine learning techniques, particularly "deep learning".The aim of the special issue titled "New Trends in Statistical Computing and Data Science" was to publish the most significant articles in these directions, that is, the current progress of statistical computing and data science. Novelty, high quality, and importance are the triptych of the special issue.

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A Zero-and-One Inflated Cosine Geometric Distribution and Its Application

Junnumtuam

Niwitpong

2022

Mathematics

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Count data containing both excess zeros and ones occur in many fields, and the zero-and-one inflated distribution is suitable for analyzing them. Herein, we construct confidence intervals (CIs) for the parameters of the zero-and-one inflated cosine geometric (ZOICG) distribution constructed by using five methods: a Wald CI based on the maximum likelihood estimate, equal-tailed Bayesian CIs based on the uniform or Jeffreys prior, and the highest posterior density intervals based on the uniform or Jeffreys prior. Their efficiencies were compared in terms of their coverage probabilities and average lengths via a simulation study. The results show that the highest posterior density intervals based on the uniform prior performed the best in most cases. The number of new daily COVID-19-related deaths in Luxembourg in 2020 involving data with a high proportion of zeros and ones were analyzed. It was found that the ZOICG model was appropriate for this scenario.

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Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

Cited by 3 publications

References 26 publications

Bayesian Inference for the Gamma Zero-Truncated Poisson Distribution with an Application to Real Data

Bayesian Inference for the Gamma Zero-Truncated Poisson Distribution with an Application to Real Data

Introduction to the Special Issue on New Trends in Statistical Computing and Data Science

A Zero-and-One Inflated Cosine Geometric Distribution and Its Application

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