(1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

Survival Distributions: Reliability Applications in the Biomedical Sciences

The weighted distributions are widely used in many fields such as medicine, ecology and reliability, to name a few, for the development of proper statistical models. Weighted distributions are milestone for efficient modeling of statistical data and prediction when the standard distributions are not appropriate. A good deal of studies related to the weight distributions have been published in the literature. In this article, a brief review of these distributions is carried out. Implications of the differing weight models for future research as well as some possible strategies are discussed. Finally, characterizations of these distributions based on a simple relationship between two truncated moments are presented.

/pdf/weighted-distributions-a-brief-review-perspective-and-1k3o0wmqm5.pdf

Weighted Distributions: A Brief Review, Perspective and Characterizations

The concept of length-biased distribution can be employed in development of proper models for life-time data. In this paper, we develop the length-biased form of Weighted Maxwell distribution (WMD). We study the statistical properties of the derived distribution including moments, moment generating function, hazard rate, reverse hazard rate, Shannon entropy and estimation of parameters

/pdf/length-biased-weighted-maxwell-distribution-iqp7thgstp.pdf

Length-biased Weighted Maxwell Distribution

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.

A New Version of Weighted Weibull Distribution: Modelling to COVID-19 Data

Recently, a new lifetime distribution known as a generalized Quasi Lindley distribution (GQLD) is suggested. In this paper, we modified the GQLD and suggested a two parameters lifetime distribution called as a weighted generalized Quasi Lindley distribution (WGQLD). The main mathematical properties of the WGQLD including the moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis, stochastic ordering, median deviation, harmonic mean, and reliability functions are derived. The model parameters are estimated by using the ordinary least squares, weighted least squares, maximum likelihood, maximum product of spacing’s, Anderson-Darling and Cramer-von-Mises methods. The performances of the proposed estimators are compared based on numerical calculations for various values of the distribution parameters and sample sizes in terms of the mean squared error (MSE) and estimated values (Es). To demonstrate the applicability of the new model, four applications of various real data sets consist of the infected cases in Covid-19 in Algeria and Saudi Arabia, carbon fibers and rain fall are analyzed for illustration. It turns out that the WGQLD is empirically better than the other competing distributions considered in this study. © 2021 the Author(s), licensee AIMS Press.

Weighted generalized quasi lindley distribution: Different methods of estimation, applications for covid-19 and engineering data

In 1999 Molodtsov [1] reach to represent a new type of mathematical concepts be called them (Soft Set) that became later on the focus of attention to many researchers in several different fields li...

Semi soft local function which generated a new topology in soft ideal spaces

The Rayleigh distribution and the distribution weighted are most important distributions used in the analysis of data modeling lifelong,  in this paper we derive the  weighted  Rayleigh  distribution  (WRD)  with estimation of its reliability using five methods are  (mle , mom, jackknife , Bayes by using Jeffery information) . we compare  between these methods by ( MSE), using program (MATLAB 2011a), results will be displayed in tables especially for the purpose of to facilitate the comparison. Keywords: Bayes Estimation, weighted distribution, Rayleigh distribution, Extension of Jeffery prior information, Maximum likelihood estimates .

/pdf/comparison-between-five-estimation-methods-for-reliability-3jte0wkmw7.pdf

Comparison between five estimation methods for reliability function of weighted Rayleigh distribution by using simulation

https://robots.iopscience.iop.org/article/10.1088/1742-6596/1795/1/012020/pdf

Three Prior Selection Estimate Reliability Function of Exponential Distribution

One of the applications of soft sets is soft local function where researchers [14] independently presented the concept of soft local function in 2014 using soft set and studied many of thei...

Soft-local function via soft point

In this study, we present estimation scale parameter of Rayleigh distribution under data the control of type II by using methods Proposed Bayesian as only prior and Double priors with a new suggestion used three Prior by using distributions Exponential and Gamma and Chi-Square. An experimental study was conducted to compare these methods and to demonstrate the efficiency of the methods proposed in practice by relying on the generated observations from Rayleigh distribution. We compare these methods by using Mean square error (MSE). We did this study by using simulation with different values parameter $(\theta)$ and different sample sizes $(\mathrm{N}=10,25,50,100)$ . It has been shown through computational results that the best way to estimate is the Bayes method proposed (BAY3).

Three Prior and Double Prior Selection to Comparison Estimate Parameter Rayleigh Distribution under Data Type II Censoring

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