A Novel Discrete Generator with Modeling Engineering, Agricultural and Medical Count and Zero-Inflated Real Data with Bayesian, and Non-Bayesian Inference

Walid Emam, Yusra Tashkandy, G.G. Hamedani, Mohamed Abdelhamed Shehab, Mohamed Ibrahim () and Haitham M. Yousof
Additional contact information
Walid Emam: Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Yusra Tashkandy: Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
G.G. Hamedani: Department of Mathematical and Statistical Sciences, Marquette University, 1313 W. Wisconsin Ave., Milwaukee, WI 53233, USA
Mohamed Abdelhamed Shehab: Department of Economics, Faculty of Commerce, Damietta University, Damietta 34517, Egypt
Mohamed Ibrahim: Department of Applied, Mathematical and Actuarial Statistics, Faculty of Commerce, Damietta University, Damiet 34517, Egypt
Haitham M. Yousof: Department of Statistics, Mathematics and Insurance, Benha University, Benha 13518, Egypt

Mathematics, 2023, vol. 11, issue 5, 1-28

Abstract: This study introduces a unique flexible family of discrete probability distributions for modeling extreme count and zero-inflated count data with different failure rates. Certain significant mathematical properties, such as the cumulant generating function, moment generating function, dispersion index, L-moments, ordinary moments, and central moment are derived. The new failure rate function offers a wide range of flexibility, including “upside down”, “monotonically decreasing”, “bathtub”, “monotonically increasing” and “decreasing-constant failure rate” and “constant”. Moreover, the new probability mass function accommodates many useful shapes including the “right skewed function with no peak”, “symmetric”, “right skewed with one peak” and “left skewed with one peak”. To obtain significant characterization findings, the hazard function and the conditional expectation of certain function of the random variable are both employed. Both Bayesian and non-Bayesian estimate methodologies are considered when estimating, assessing, and comparing inferential efficacy. The Bayesian estimation approach for the squared error loss function is suggested, and it is explained. Markov chain Monte Carlo simulation studies are performed using the Metropolis Hastings algorithm and the Gibbs sampler to compare non-Bayesian vs. Bayesian results. Four real-world applications of count data sets are used to evaluate the Bayesian versus non-Bayesian techniques. Four more real count data applications are used to illustrate the significance and versatility of the new discrete class.

Keywords: discretization; Bayesian estimation; metropolis-hastings; maximum likelihood; Cramér-von-Mises; Markov chain Monte Carlo; squared error loss function; zero-inflated engineering count data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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