Statistical Analysis of COVID-19 Data: Using A New Univariate and Bivariate Statistical Model

Bantan, Rashad A. R.; Shafiq, Shakaiba; Tahir, M. H.; Elhassanein, Ahmed; Jamal, Farrukh; Almutiry, Waleed; Elgarhy, Mohammed

doi:10.1155/2022/2851352

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Researchers, such as those in [1,2,32], studied the data by dividing it by 100 to rescale it on the unit interval. The GUHLG distribution is fitted to the data, and its performance is compared to that of the UHLG distribution, beta distribution, Kumaraswamy distribution, unit power Weibull (UPW) distribution (see [33]), log-XLindley (LXL) distribution (see [34]), log-Bilal (LB) distribution (see [35]), unit Burr XII (UBXII) distribution (see [36]), unit Burr III (UBIII) distribution (see [37]), unit Weibull (UW) distribution (see [5]) and exponentiated Topp-Leone (ETL) distribution (see [38]). The comparison benchmarks are the −2 , Akaike information criterion (AIC), AIC difference (∆AIC), Akaike weights (ω), Bayesian information criterion (BIC) and Kolmogorov-Smirnov (KS) statistic.…”

Section: Univariate Applicationmentioning

confidence: 99%

Generalized Unit Half-Logistic Geometric Distribution: Properties and Regression with Applications to Insurance

Nasiru¹,

Chesneau

Abubakari³

et al. 2023

Analytics

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The use of distributions to model and quantify risk is essential in risk assessment and management. In this study, the generalized unit half-logistic geometric (GUHLG) distribution is developed to model bounded insurance data on the unit interval. The corresponding probability density function plots indicate that the related distribution can handle data that exhibit left-skewed, right-skewed, symmetric, reversed-J, and bathtub shapes. The hazard rate function also suggests that the distribution can be applied to analyze data with bathtubs, N-shapes, and increasing failure rates. Subsequently, the inferential aspects of the proposed model are investigated. In particular, Monte Carlo simulation exercises are carried out to examine the performance of the estimation method by using an algorithm to generate random observations from the quantile function. The results of the simulation suggest that the considered estimation method is efficient. The univariate application of the distribution and the multivariate application of the associated regression using risk survey data reveal that the model provides a better fit than the other existing distributions and regression models. Under the multivariate application, we estimate the parameters of the regression model using both maximum likelihood and Bayesian estimations. The estimates of the parameters for the two methods are very close. Diagnostic plots of the Bayesian method using the trace, ergodic, and autocorrelation plots reveal that the chains converge to a stationary distribution.

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Section: Univariate Applicationmentioning

confidence: 99%

Generalized Unit Half-Logistic Geometric Distribution: Properties and Regression with Applications to Insurance

Nasiru¹,

Chesneau

Abubakari³

et al. 2023

Analytics

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“…Here the new BISPTIHLIW is used to model the mortality COVID-19 data for Italy and Canada in the period from 1 April to 21 August 2020. Data is available at https://github.com/CSSEGISandData/ COVID-19/ and in [32,33]. We consider the two dimensional random variable with observed values the mortality COVID-19 data.…”

Section: Mortality Covid-19 Datamentioning

confidence: 99%

Untitled

2022

AJAMS

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This manuscript presents a new univariate six parameters type I half-logistic inverse Weibull distribution. Explicit expressions for the quantile function, the moments, the moment generating function and the maximum likelihood estimators are formulated. Simulation is employed to investigate the goodness of fit and to discuss the behaviour of the new model. Competitive models are compared via real data. The univariate one is used as a base line to construct a bivariate one named bivariate six parameters type I half-logistic inverse Weibull distribution. Mathematical properties of the new bivariate distrib-ution are investigated. The goodness of fit and the model performance are discussed via simulation. COVID-19 mortality data for Italy and Canada are treated as a bivariate random variable to prove the applicability of the new bivariate distribution.

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“…Unit power Weibull [27] the constrained interval, effectively representing the data's characteristics. Unit distributions capture the essence of bounded data and broaden analytical possibilities, offering a versatile toolkit for addressing various real-world challenges.…”

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confidence: 99%

Study of a bounded interval perks distribution with quantile regression analysis

Al‐Essa,

Shafiq,

Ozonur

et al. 2024

Statistical Analysis

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In this article, a novel bounded interval model called the unit‐Perks model is developed by suitably transforming the positive random variable of the Perks distribution. Numerous statistical features of the bounded interval Perks model are being explored based on the expansion of the density function. Eight distinct estimation approaches are being used to estimate the parameters of the unit‐Perks model. A throughout simulation analysis is also included to evaluate the precision of the resulting estimators from eight estimating approaches. Two real bounded interval data sets are being utilized to investigate the practical applicability of the unit‐Perks model. A comparison is also made to determine which method of estimation works better for the given model. According to a comparison of eight different estimation approaches, the maximum likelihood estimation approach outperformed than the other seven estimating approaches. The unit‐perks model is then used to introduce the quantile regression model named as quantile unit‐Perks distribution. Application to real data set for the quantile unit‐Perks distribution is also performed. The quantile residuals are used for the residual analysis of the fitted regression model. On the basis of mathematical, computational, and pictorial evidences, it is concluded that the presented model exhibited greater modeling capabilities.

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Statistical Analysis of COVID-19 Data: Using A New Univariate and Bivariate Statistical Model

Cited by 8 publications

References 30 publications

Generalized Unit Half-Logistic Geometric Distribution: Properties and Regression with Applications to Insurance

Generalized Unit Half-Logistic Geometric Distribution: Properties and Regression with Applications to Insurance

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Study of a bounded interval perks distribution with quantile regression analysis

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