Univariate Probability-G Classes for Scattered Samples under Different Forms of Hazard: Continuous and Discrete Version with Their Inferences Tests

Mohamed S. Eliwa, Muhammad H. Tahir, Muhammad A. Hussain, Bader Almohaimeed (), Afrah Al-Bossly and Mahmoud El-Morshedy
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Mohamed S. Eliwa: Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia
Muhammad H. Tahir: Department of Statistics, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Muhammad A. Hussain: Department of Statistics, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Bader Almohaimeed: Department of Mathematics, College of Science, Qassim University, Buraydah 51482, Saudi Arabia
Afrah Al-Bossly: Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Mahmoud El-Morshedy: Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Mathematics, 2023, vol. 11, issue 13, 1-24

Abstract: In this paper, we define a new generator to propose continuous as well as discrete families (or classes) of distributions. This generator is used for the DAL model (acronym of the last names of the authors, Dimitrakopoulou, Adamidis, and Loukas). This newly proposed family may be called the new odd DAL (NODAL) G-class or alternate odd DAL G-class of distributions. We developed both a continuous as well as discrete version of this new odd DAL G-class. Some mathematical and statistical properties of these new G-classes are listed. The estimation of the parameters is discussed. Some structural properties of two special models of these classes are described. The introduced generators can be effectively applied to discuss and analyze the different forms of failure rates including decreasing, increasing, bathtub, and J-shaped, among others. Moreover, the two generators can be used to discuss asymmetric and symmetric data under different forms of kurtosis. A Monte Carlo simulation study is reported to assess the performance of the maximum likelihood estimators of these new models. Some real-life data sets (air conditioning, flood discharges, kidney cysts) are analyzed to show that these newly proposed models perform better as compared to well-established competitive models.

Keywords: statistical model; odd G-class; discrete generators; failure analysis; dispersion phenomena; estimation; computer simulation; comparative study; statistics and numerical data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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