ABSTRACT
A novel distribution, the Maxwell-Burr X (M-BX) distribution, was proposed. This distribution was an extension of the Burr X distribution by applying the Maxwell generalized family of distributions. The cumulative distribution function, probability density function, survival function, hazard function and quantile function of the M-BX distribution were defined. Some important properties and the parameters of its estimates were discussed. A simulation study was conducted from the basis of quantile function to ascertain the performance of maximum likelihood estimators. The M-BX distribution were also applied to model two lifetime data sets relating to the COVID-19 mortality rate in Thailand during different periods to express the flexibility of the distribution against other competing distributions. According to information criteria, AIC, CAIC, BIC, and HQIC, the M-BX distribution gave the best fit among all chosen distributions. © 2023, Thai Statistical Association. All rights reserved.
ABSTRACT
This paper proposes the 4-parameter odd Lomax generalized exponential distribution for the study of engineering and COVID-19 data. The statistical and mathematical properties of this distribution such as a linear representation of the probability density function, survival function, hazard rate function, moments, quantile function, order statistics, entropy, mean deviation, characteristic function and average residual life function are established. The estimates of parameters of the proposed distribution are obtained using maximum likelihood estimation (MLE), Maximum product spacings (MPS), least-square estimation (LSE), and Cramer-Von-Mises estimation (CVME) methods. A Monte-Carlo simulation experiment is carried out to study the performance of MLEs. The applicability of the proposed distribution is evaluated using two real datasets related to engineering and COVID-19 death cases. All the computational work was performed in R programming software. © 2022, Pakistan Journal of Statistics and Operation Research. All Rights Reserved.
ABSTRACT
Combating SARS-CoV-2 is the first concern and goal of the whole world faced with the global health crisis. Since 2019, the SARS-CoV-2 infection (COVID-19) and even mutated infection cases have been increasing rapidly. From 2019 through 27 August 2021, a total of 214,468,601 individuals were confirmed cases of SARS-CoV-2, including 4,470,969 death toll. Some of these individuals were able to access treatment and some could not, but for a while there was complete uncertainty. It was not known whether those who accessed treatment were lucky, but treatment was based on trial and error because of this uncertainty around the world until data was collected. Therefore, the aim of this study was to model SARS-CoV-2 infectious disease progression from the date of polymerase chain reaction (PCR) test to the date of negative outcome via Bayesian multi-state model approaches considering risk factors such as gender, age, and antiviral treatment. Data from 746 inpatients were collected from August 1st until the December 1st, 2020. For the multi-state model, five various discrete states were selected according to the Republic of Turkey Ministery of Health treatment algorithm. The results showed that Bayesian multi-state models with the Weibull distributed baseline hazard function were more appropriate models in the presence of risk factors and antiviral treatment.
ABSTRACT
In this study, we will look at a new flexible model known as the new double-weighted Weibull distribution. The new Weibull double-weighted distribution model is highly versatile because numerous submodels are included. The proposed model is very flexible because its density function has many shapes;it can be right skewness, decreasing, and unimodal. Also, the hazard rate function can be increasing, decreasing, up-side-down, and J-shaped. Diverse features of the novel are computed. These qualities include moments, incomplete moments, and Lorenz and Bonferroni curves and quantiles, as well as entropy and order statistics. The maximum likelihood approach is used to estimate the model's parameters. In order to evaluate the accuracy and performance of maximum likelihood estimators, simulation data are presented. The utility and adaptability of the proposed model are demonstrated by utilizing three significant datasets: daily fatalities confirmed cases of COVID-19 in Egypt and Georgia and relief times of twenty patients using an analgesic. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
The Weibull regression model is one of the most important parametric regression models. Because of the knowledge of the probability distribution of the response variable following the Weibull distribution, which facilities the possibility of estimating the regression parameters based on the baseline hazard function. It is estimated by estimating the parameters of the Weibull distribution using the maximum likelihood estimation method. The R software was used for the purpose of estimating the regression coefficients and identifying the most significant features that model the outcome. In this paper, we investigate the factors affecting the progression of Corona virus patients from Al-Shiffa Hospital in the city of Mosul. In addition, this study focused on patients who were in a critical condition, and whose cases necessitated their monitoring during their stay under the artificial respiration machine Continues Positive Airway Pressure (CPAP). The six variables were taken as the most influential on the injury case and it was found that the most influential variables were Remdesivir and O2 using some statistical criteria. © 2021 IEEE.