The Discrete Type-II Half-Logistic Exponential Distribution with Applications to COVID-19 Data

Haq, Muhammad Ahsan ul; Babar, Ayesha; Hashmi, Sharqa; Alghamdi, Abdulaziz; Afify, Ahmed Z.

doi:10.18187/pjsor.v17i4.3772

Cited by 11 publications

(2 citation statements)

References 17 publications

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“…The efficiency of this model against other competitive's distributions, such as discrete Burr-Hatke (DBH), discrete Rayleigh (DR), discrete inverse Rayleigh (DIR) and discrete Pareto (DPr) is proven. The results for the competitive distributions taken from [11].…”

Section: Applicationsmentioning

confidence: 99%

A Discrete Odd Lindley Half-Logistic Distribution with Applications

Shamlan,

Baaqeel,

Fayomi

2024

J. Phys.: Conf. Ser.

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In the reality, the most of lifetime data is discrete in nature, despite it is known that this data is continuous. As a consequence, the tendency to convert the continuous lifetime distributions to its discrete counterpart has raised. The nature of the most lifetime data in real life is discrete, thus, many of researchers interested to convert. This article aims to introduce a new one-parameter discrete distribution, namely Discrete Odd Lindley Half-Logistic (DOLiHL) distribution. The DOLiHL distribution is derived by discretizing the analogue continuous distribution, using a survival discretization technique. An intensive study of this distribution is provided, including characteristic functions and statistical properties. Moreover, the unknown parameter is estimated using different methods of estimation namely, the maximum likelihood method, method of moments, least squares method and Cramer-von Mises minimum distance estimation method. A simulation study is conducted to examine the distribution’s behavior and compare between the estimation methods at different sample sizes. To evaluate the efficiency of this distribution against other competitive distributions, an implementation on real data in different fields is performed. In conclude, the DOLiHL distribution has assured its efficiency of fitting in lifetime and count data from various fields in real life.

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Section: Applicationsmentioning

confidence: 99%

A Discrete Odd Lindley Half-Logistic Distribution with Applications

Shamlan,

Baaqeel,

Fayomi

2024

J. Phys.: Conf. Ser.

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“…In past several discrete distributions have been introduced and studied, such as discrete Weibull distribution discrete beta exponential distribution by Nekoukhou et al 4 , two-parameter discrete Lindley distribution by Hussain et al 5 , Discrete Marshall–Olkin inverse Toppe–Leone with application to COVID-19 data has been obtained by Almetwally et al 6 . discrete weighted exponential distribution by Rasekhi et al 7 exponentiated discrete Lindley distribution by El-Morshedy et al 6 , discrete Burr Hutke distribution by El-Morshedy et al 8 , discrete Marshall–Olkinin Weibull distribution by Opone et al 9 ), see Almetwally et al 10 , discrete Marshall–Olkinin alpha power inverse Lomax distribution by Almetwally et al 11 , discrete inverted Topp–Leone distribution by Eldeeb et al 12 , discrete Ramos–Louzada distribution by Eldeeb et al 13 , discrete type-II half logistic exponential distribution Ahsan-ul-Haq et al 14 , discrete power-Ailamujia distribution by Alghamdi et al 15 , Poisson XLindley distribution Ahsan-ul-Haq et al 16 , Poisson moment exponential distribution Ahsan-ul-Haq 17 and discrete moment exponential distribution by Afify et al 18 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Covid-19 data using discrete Marshall–Olkinin Length Biased Exponential: Bayesian and frequentist approach

Aljohani

Haq

Zafar

et al. 2023

Sci Rep

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The paper presents a novel statistical approach for analyzing the daily coronavirus case and fatality statistics. The survival discretization method was used to generate a two-parameter discrete distribution. The resulting distribution is referred to as the "Discrete Marshall–Olkin Length Biased Exponential (DMOLBE) distribution". Because of the varied forms of its probability mass and failure rate functions, the DMOLBE distribution is adaptable. We calculated the mean and variance, skewness, kurtosis, dispersion index, hazard and survival functions, and second failure rate function for the suggested distribution. The DI index demonstrates that the proposed model can represent both over-dispersed and under-dispersed data sets. We estimated the parameters of the DMOLBE distribution. The behavior of ML estimates is checked via a comprehensive simulation study. The behavior of Bayesian estimates is checked by generating 10,000 iterations of Markov chain Monte Carlo techniques, plotting the trace, and checking the proposed distribution. From simulation studies, it was observed that the bias and mean square error decreased with an increase in sample size. To show the importance and flexibility of DMOLBE distribution using two data sets about deaths due to coronavirus in China and Pakistan are analyzed. The DMOLBE distribution provides a better fit than some important discrete models namely the discrete Burr-XII, discrete Bilal, discrete Burr-Hatke, discrete Rayleigh distribution, and Poisson distributions. We conclude that the new proposed distribution works well in analyzing these data sets. The data sets used in the paper was collected from 2020 year.

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On Poisson Moment Exponential Distribution with Applications

Haq

2022

Ann. Data. Sci.

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The Discrete Type-II Half-Logistic Exponential Distribution with Applications to COVID-19 Data

Cited by 11 publications

References 17 publications

A Discrete Odd Lindley Half-Logistic Distribution with Applications

A Discrete Odd Lindley Half-Logistic Distribution with Applications

Analysis of Covid-19 data using discrete Marshall–Olkinin Length Biased Exponential: Bayesian and frequentist approach

On Poisson Moment Exponential Distribution with Applications

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