scholarly journals MODELING AND COMPARISON OF MAXIMUM LIKELIHOOD AND MEDIAN RANK REGRESSION METHODS WITH FRÉCHET DISTRIBUTION

2020 ◽  
Vol 2 (2) ◽  
pp. 159-168
Author(s):  
Maryam Firdos ◽  
Kamran Abbas ◽  
Shahab Ahmed Abbasi ◽  
Nosheen Yousaf Abbasi
Keyword(s):  
Maximum Likelihood ◽  
Rank Regression ◽  
Regression Methods ◽  
Fréchet Distribution ◽  
Median Rank ◽  
Frechet Distribution

Properties and methods of estimation for a bivariate exponentiated Fréchet distribution

2020 ◽  
Vol 70 (5) ◽  
pp. 1211-1230
Author(s):  
Abdus Saboor ◽  
Hassan S. Bakouch ◽  
Fernando A. Moala ◽  
Sheraz Hussain
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Superior Performance ◽  
Estimation Methods ◽  
Conditional Probability Density ◽  
Data Set ◽  
Fréchet Distribution ◽  
Frechet Distribution

AbstractIn this paper, a bivariate extension of exponentiated Fréchet distribution is introduced, namely a bivariate exponentiated Fréchet (BvEF) distribution whose marginals are univariate exponentiated Fréchet distribution. Several properties of the proposed distribution are discussed, such as the joint survival function, joint probability density function, marginal probability density function, conditional probability density function, moments, marginal and bivariate moment generating functions. Moreover, the proposed distribution is obtained by the Marshall-Olkin survival copula. Estimation of the parameters is investigated by the maximum likelihood with the observed information matrix. In addition to the maximum likelihood estimation method, we consider the Bayesian inference and least square estimation and compare these three methodologies for the BvEF. A simulation study is carried out to compare the performance of the estimators by the presented estimation methods. The proposed bivariate distribution with other related bivariate distributions are fitted to a real-life paired data set. It is shown that, the BvEF distribution has a superior performance among the compared distributions using several tests of goodness–of–fit.

scholarly journals Gompertz Fréchet Strength of a component between two stresses Reliability Estimation

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Sarah Adnan ◽  
Nada Sabah Karam
Keyword(s):  
Maximum Likelihood ◽  
Sampling Methods ◽  
Reliability Estimation ◽  
Least Square ◽  
Scale Parameters ◽  
Fréchet Distribution ◽  
Distribution Parameters ◽  
Component Strength ◽  
Frechet Distribution ◽  
Better Than

In this paper, the reliability of the stress-strength model is derived for probability P( <X< ) of a component strength X between two stresses ,  , when X and ,  are independent Gompertz Fréchet distribution with unknown and known shape parameters and common known scale parameters. Different methods used to estimate R and Gompertz Fréchet distribution parameters which are [Maximum Likelihood, Least square, Weighted Least square, Regression and Ranked set sampling methods], and the comparison between these estimations by simulation study based on mean square error criteria. The comparison confirms that the performance of the maximum likelihood estimator works better than the other estimators.

scholarly journals Beta Generalized Exponentiated Frechet Distribution with Applications

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 687-697
Author(s):  
Majdah M. Badr
Keyword(s):  
Maximum Likelihood ◽  
Hazard Rate ◽  
Information Matrix ◽  
Model Parameters ◽  
Fréchet Distribution ◽  
Method Of Maximum Likelihood ◽  
Frechet Distribution ◽  
Fisher's Information

Abstract In this article we introduce a new six - parameters model called the Beta Generalized Exponentiated-Frechet (BGEF) distribution which exhibits decreasing hazard rate. Many models such as Beta Frechet (BF), Beta ExponentiatedFrechet (BEF), Generalized Exponentiated-Frechet (GEF), ExponentiatedFrechet (EF), Frechet (F) are sub models. Some of its properties including rth moment, reliability and hazard rate are investigated. The method of maximum likelihood isproposed to estimate the model parameters. The observed Fisher’s information matrix is given. Moreover, we give the advantage of the (BGEF) distribution by an application using two real datasets

scholarly journals The four-parameter Fréchet distribution: Properties and applications

2020 ◽  
pp. 249-264
Author(s):  
Mohamed Hamed ◽  
Fahad Aldossary ◽  
Ahmed Z. Afify
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Model Parameters ◽  
Fréchet Distribution ◽  
Mathematical Properties ◽  
Modeling Data ◽  
Frechet Distribution

In this article, we propose a new four-parameter Fréchet distribution called the odd Lomax Fréchet distribution. The new model can be expressed as a linear mixture of Fréchet densities. We provide some of its mathematical properties. The estimation of the model parameters is performed by the maximum likelihood method. We illustrate the good performance of the maximum likelihood estimates via a detailed numerical simulation study. The importance and usefulness of the proposed distribution for modeling data are illustrated using two real data applications.

scholarly journals Gompertz Fréchet stress-strength Reliability Estimation

2021 ◽  
pp. 4892-4902
Author(s):  
Sarah A. Jabr ◽  
Nada S. Karam
Keyword(s):  
Maximum Likelihood ◽  
Reliability Estimation ◽  
Least Square ◽  
Shape Parameters ◽  
Likelihood Estimator ◽  
Fréchet Distribution ◽  
Distribution Parameters ◽  
Estimate Reliability ◽  
Frechet Distribution ◽  
Better Than

In this paper, the reliability of the stress-strength model is derived for probability P(Y<X) of a component having its strength X exposed to one independent stress Y, when X and Y are following Gompertz Fréchet distribution with unknown shape parameters and known parameters . Different methods were used to estimate reliability R and Gompertz Fréchet distribution parameters, which are maximum likelihood, least square, weighted least square, regression, and ranked set sampling. Also, a comparison of these estimators was made by a simulation study based on mean square error (MSE) criteria. The comparison confirms that the performance of the maximum likelihood estimator is better than that of the other estimators.

New Acceptance Sampling Plans Based on Percentiles for Exponentiated Fréchet Distribution

2016 ◽  
Vol 31 (1) ◽  
Author(s):  
Gadde Srinivasa Rao ◽  
Kanaparthi Rosaiah ◽  
Mothukuri Sridhar Babu ◽  
Devireddy Charanaudaya Sivakumar
Keyword(s):  
Operating Characteristic ◽  
Real Data ◽  
Acceptance Sampling ◽  
Producer’S Risk ◽  
Data Set ◽  
Sampling Plans ◽  
Fréchet Distribution ◽  
Acceptance Sampling Plans ◽  
Characteristic Values ◽  
Frechet Distribution

AbstractIn this article, acceptance sampling plans are developed for the exponentiated Fréchet distribution based on percentiles when the life test is truncated at a pre-specified time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given customer's risk and producer's risk simultaneously. The operating characteristic values of the sampling plans are presented. One example with real data set is also given as an illustration.

Sign in / Sign up

Export Citation Format

Share Document