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In this article, a new class of weighted exponential distribution called Entropy-Based Exponential weighted distribution (EBEWD) is proposed. The main idea of the new lifetime distribution is in using the Shannon entropy measure as a weighted function. The mathematical properties of the EBEWD, such as reliability measures, moment measures, variability measures, shapes measures, entropy measures and Fisher’s information, were derived. The unknown parameters of the proposed distribution were estimated using the maximum likelihood estimation method. The efficiency and flexibility of the EBEWD were examined numerically and then by a real-life data application. The results revealed that the EBEWD outperforms some existing distributions in terms of their test statistics.

Motivated mainly by lifetime issues, a new lifetime distribution coined ``Discrete Poisson-Gold distribution'' is introduced in this paper. Different structural properties of the new distribution are derived including moment generating function and the $r^{th}$ moment and others are presented. In addition, we discussed various important mathematical properties of the new distribution including estimation procedures for estimating the distribution parameters using the maximum likelihood and method of moments. The usefulness and credibility of the distribution are illustrated by means of two real-data applications to show its superior performance over some other well-known lifetime distributions and to prove its versatility in practical applications.

Background: Viral cause of sporadic breast cancer (SBC) has been suggested based on the experimental murine model of mammary tumor caused by mouse mammary tumor virus (MMTV), Epstein-Barr virus (EBV), and human papillomavirus (HPV). While some studies have demonstrated the presence of viral sequences of MMTV, HPV, and EBV in breast cancer cells, others failed. These contradictions may be attributed to the geographical distribution of breast cancer incidence and/or technical variations. In the current study, we aimed to investigate the correlation of MMTV, HPV, and EBV infections with the development of breast cancer in Jordanian patients. Methods: One hundred SBC tissue samples were subjected to laser capture microdissection for the selection of tumor cells populations. Fluorescence polymerase chain reaction (PCR) was used to detect the presence of the MMTV env-like sequences. Real-time PCR was used for HPV and EBV detection, and EBV was further confirmed by chromogen in situ hybridization (CISH). Results: Mouse mammary tumor virus, HPV, and EBV were detected in SBC in 11%, 21%, and 23%, respectively. Only 3 of 52 (5.7%) positive cases demonstrated multiple virus infections. However, 49 of 52 (94%) of the positive cases revealed the presence of 1 type of viral sequences. Consequently, 52% of the studied breast cancer cases were infected with at least 1 type of the aforementioned viruses. Conclusions: The current cohort suggests that MMTV, HPV, and EBV have a potential role in the development of breast cancer and adding more reasons to proceed with the quest of a possible viral origin of breast cancer.

While there is a growing body of research on the relationship between smoking and COVID-19 outcomes, there is a lack of comprehensive studies focusing specifically on the Arab world. Understanding the potential impact of smoking on COVID-19 death rates in this region is crucial for informing public health interventions and policies. This article discusses the relationship between the smoking rate and the COVID-19 death rate in the Arab world. The data was gathered from the World Health Organization (WHO) records. The measurement error model (MEM) is used to study the relationship between smoking rate and COVID-19, considering the errors in both variables. Different estimation methods including the maximum likelihood estimation (MLE), Wald-type methods, and repeated median estimation methods, were used to fit the MEM model to the data. The results showed that Jordan and Lebanon, respectively, had the highest smoking rates while Yemen, Egypt, and Algeria, respectively, had the highest COVID-19 death within the Arab countries. Fitting the data to MEM, the data analysis showed that Smoking has a positive effect on COVID-19. [ABSTRACT FROM AUTHOR]

In December 2019, a cluster of fatal pneumonia cases presented in Wuhan, China. They were caused by a previously unknown coronavirus. All patients had been associated with the Wuhan Wholefood market, where seafood and live animals are sold. The virus spread rapidly and public health authorities in China initiated a containment effort. However, by that time, travelers had carried the virus to many countries, sparking memories of the previous coronavirus epidemics, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and causing widespread media attention and panic. Based on clinical criteria and available serological and molecular information, the new disease was called coronavirus disease of 2019 (COVID-19), and the novel coronavirus was called SARS Coronavirus-2 (SARS-CoV-2), emphasizing its close relationship to the 2002 SARS virus (SARS-CoV). The scientific community raced to uncover the origin of the virus, understand the pathogenesis of the disease, develop treatment options, define the risk factors, and work on vaccine development. Here we present a summary of current knowledge regarding the novel coronavirus and the disease it causes.

The classical maximum likelihood estimation fails to estimate the simple linear measurement error model, with or without equation error, unless additional assumptions are made about the structural parameters. In the literature there are six different assumptions that could be added in order to solve the measurement error models. In this paper, we proposed an entropy-type estimator based on the generalized maximum entropy estimation approach, which allows one to abstract away from the additional assumptions that are made in the classical method. Monte Carlo experiments were carried out in order to investigate the performance of the proposed estimators. The simulation results showed that the entropy-type estimator of unknown parameters has outperformed the classical estimators in terms of mean square error criterion.

In this paper an estimation method based on double phase sampling is proposed to improve the efficiency of estimating the population mean. An extension is presented for the bivariate case to estimate the parameters of the simple linear regression model. Conclusions of this study show that using the proposed method with symmetric populations, the estimator of the population mean is unbiased and more efficient than the traditional one that is based on a simple random sample. Results for the standard uniform and the exponential distribution are given. Simulation results show that the proposed method is also more efficient than the traditional one in case of estimating the regression parameters. An application to a real data set is also given.

This article proposes a new estimation method to fit the structural regression model when the variables are subject to errors. The new estimation method is the extension of theWald estimation method and involves iterative process. Several Monte Carlo simulation experiments were used to study the performance of the proposed estimators. The results were compared with the classical Wald estimation method in terms of its root mean square error (RMSE). In addition, an application for examining the relationships between Jordan's national gross domestic product (GDP) and its human development index (HDI) was presented. Numerical results showed that the GDP and HDI have a strong positive and significant correlation. Moreover, the proposed procedures with different subgroup sizes (r =3 and r =4) gave more accurate estimators than the classical estimation methods in fitting the relationships between GDP and HDI. [ABSTRACT FROM AUTHOR]

In environmental and many other areas, the main focus of survey is to measure elements using an efficient and cost-effective sampling technique. One way to reach that is by using Ranked set sampling (RSS). RSS is an alternative sampling technique that can be advantageous when measuring the variable of interest is either costly or time-consuming but ranking small sets of units according to the character under investigation by eye or other methods not requiring actual quantifications. The purpose of this article is to introduce a new modification of RSS to estimate the mean of the target population. This proposed technique is a double-stage approach that combines median RSS (MRSS) and MiniMax RSS (MMRSS). The performance of the empirical mean and variance estimators based on the proposed technique are compared with their counterparts in Double RSS (DRSS), Extreme RSS (ERSS), Double Extreme RSS (DERSS), MMRSS, RSS, and simple random sampling (SRS) via Monte Carlo simulation. Simulation results revealed that this new modification is almost always more efficient than their counterparts using MMRSS and SRS, while it is more efficient than RSS in many cases especially when the distribution is asymmetric. [ABSTRACT FROM AUTHOR]

In this paper, truncated lifetime testing is considered, and new single acceptance sampling plans (SASP) are proposed assuming that the lifetime distribution is the q-Weibull distribution of a product. Assuming a finite population size (limited population size, N); the inspection process for a single sampling plan is ASP(n,p) begins by choosing a simple random sample from a given lot, then based on pre-assigned quality standards, the manager will decide to reject the lot if some items fail to meet the pre-assigned quality standards. Since the population is limited; then this experiment meets the hypergeometric distribution assumptions. The hypergeometric theory is applied to compute the probability of acceptance, and the procedure is used to compute the minimum sample size and the operating characteristics of the sampling plans. Also, a real data analysis is given to illustrate the applicability of the proposed plan in the industry. [ABSTRACT FROM AUTHOR]