Open AccessThe Discrete Type-II Half-Logistic Exponential Distribution with Applications to COVID-19 Data
Author(s) -
Muhammad Ahsan ul Haq,
Ayesha Babar,
Sharqa Hashmi,
Abdulaziz Alghamdi,
Ahmed Z. Afify
Publication year - 2021
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v17i4.3772
Subject(s) - mathematics , discrete time and continuous time , discretization , exponential family , estimator , pareto distribution , distribution (mathematics) , rayleigh distribution , statistics , discrete modelling , discrete system , algorithm , mathematical analysis , probability density function
We propose a new two-parameter discrete model, called discrete Type-II half-logistics exponential (DTIIHLE) distribution using the survival discretization approach. The DTIIHLE distribution can be utilized to model COVID-19 data. The model parameters are estimated using the maximum likelihood method. A simulation study is conducted to evaluate the performance of the maximum likelihood estimators. The usefulness of the proposed distribution is evaluated using two real-life COVID-19 data sets. The DTIIHLE distribution provides a superior fit to COVID-19 data as compared with competitive discrete models including the discrete-Pareto, discrete Burr-XII, discrete log-logistic, discrete-Lindley, discrete-Rayleigh, discrete inverse-Rayleigh, and natural discrete-Lindley.