scholarly journals Estimation of Stress Strength Reliability in Single Component Models for Different Distributions

2019 ◽  
pp. 1-10
Author(s):  
Nafeesa Bashir ◽  
Raeesa Bashir ◽  
T. R. Jan ◽  
Shakeel A. Mir
Keyword(s):  
Likelihood Estimation ◽  
Inverse Power ◽  
Reliability Parameter ◽  
Strength Parameters ◽  
Lindley Distribution ◽  
Power Lindley Distribution ◽  
Method Of Maximum Likelihood ◽  
R Programming ◽  
Component Models ◽  
Inverse Lindley Distribution

This paper aims to estimate the stress-strength reliability parameter R = P(Y < X), considering the two different cases of stress strength parameters, when the strength ‘X’ follows exponentiated inverse power Lindley distribution ,extended inverse Lindley and Stress ‘Y’ follows inverse power Lindley distribution and inverse Lindley distribution. The method of maximum likelihood estimation is used to obtain the reliability estimators. Illustrations are provided using R programming.

scholarly journals A Lomax-inverse Lindley Distribution: Model, Properties and Applications to Lifetime Data

2019 ◽  
pp. 1-28
Author(s):  
Terna Godfrey Ieren ◽  
Peter Oluwaseun Koleoso ◽  
Adana’a Felix Chama ◽  
Innocent Boyle Eraikhuemen ◽  
Nasiru Yakubu
Keyword(s):  
Likelihood Estimation ◽  
Quantile Function ◽  
Distribution Model ◽  
Lindley Distribution ◽  
Limiting Behavior ◽  
Proposed Model ◽  
Method Of Maximum Likelihood ◽  
Inverse Lindley Distribution ◽  
New Distribution ◽  
Better Than

This article proposed a new extension of the Inverse Lindley distribution called “Lomax-Inverse Lindley distribution” which is more flexible compared to the Inverse Lindley distribution and other similar models. The paper derives and discusses some Statistical properties of the new distribution which include the limiting behavior, quantile function, reliability functions and distribution of order statistics. The parameters of the new model are estimated by method of maximum likelihood estimation. Conclusively, three lifetime datasets were used to evaluate the usefulness of the proposed model and the results indicate that the proposed extension is more flexible and performs better than the other distributions considered in this study.

scholarly journals Uma proposta para identificação de outliers multivariados

2018 ◽  
Vol 40 ◽  
pp. 40
Author(s):  
Josino José Barbosa ◽  
Tiago Martins Pereira ◽  
Fernando Luiz Pereira de Oliveira
Keyword(s):  
Probability Distributions ◽  
Estimation Method ◽  
Real Data ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Inverse Power ◽  
Test Statistic ◽  
Lindley Distribution ◽  
Power Lindley Distribution ◽  
Inverse Lindley Distribution

In the last years several probability distributions have been proposed in the literature, especially with the aim of obtaining models that are more flexible relative to the behaviors of the density and hazard rate functions. For instance, Ghitany et al. (2013) proposed a new generalization of the Lindley distribution, called power Lindley distribution, whereas Sharma et al. (2015a) proposed the inverse Lindley distribution. From these two generalizations Barco et al. (2017) studied the inverse power Lindley distribution, also called by Sharma et al. (2015b) as generalized inverse Lindley distribution. Considering the inverse power Lindley distribution, in this paper is evaluate the performance, through Monte Carlo simulations, with respect to the bias and consistency of nine different methods of estimations (the maximum likelihood method and eight others based on the distance between the empirical and theoretical cumulative distribution function). The numerical results showed a better performance of the estimation method based on the Anderson-Darling test statistic. This conclusion is also observed in the analysis of two real data sets.

scholarly journals Marshall-Olkin extended inverse power Lindley distribution with applications

2018 ◽  
Author(s):  
Rafif Hibatullah ◽  
Yekti Widyaningsih ◽  
Sarini Abdullah
Keyword(s):  
Inverse Power ◽  
Lindley Distribution ◽  
Power Lindley Distribution

The inverse power Lindley distribution in the presence of left-censored data

2017 ◽  
Vol 45 (11) ◽  
pp. 2081-2094
Author(s):  
Emílio A. Coelho-Barros ◽  
Josmar Mazucheli ◽  
Jorge A. Achcar ◽  
Kelly Vanessa Parede Barco ◽  
José Rafael Tovar Cuevas
Keyword(s):  
Censored Data ◽  
Inverse Power ◽  
Lindley Distribution ◽  
Power Lindley Distribution

scholarly journals A New Two-Parameter Lindley Distribution

2020 ◽  
Vol 1 ◽  
pp. 33-42
Author(s):  
Rama Shanker ◽  
Umme Habibah Rahman
Keyword(s):  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Likelihood Estimation ◽  
Lifetime Data ◽  
Lindley Distribution ◽  
Method Of Maximum Likelihood ◽  
Two Parameter ◽  
Family Of Distributions

In this paper, a new two-parameter Lindley distribution has been proposed. Descriptive statistical properties along with order statistics, Fisher information matrix and confidence interval of the proposed distribution have been discussed. Parameters are estimated by the method of Maximum Likelihood estimation. A real lifetime data has been presented to test the goodness of fit of the proposed distribution over other one parameter and two –parameter Lindley family of distributions.

scholarly journals Length biased Weighted New Quasi Lindley Distribution: Statistical Properties and Applications

2021 ◽  
pp. 123-136
Author(s):  
Rashid A. Ganaie ◽  
V. Rajagopalan
Keyword(s):  
Information Matrix ◽  
Real Life ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Data Sets ◽  
Lindley Distribution ◽  
Method Of Maximum Likelihood ◽  
Special Case ◽  
Biased Distribution ◽  
Length Biased Distribution

In this Paper, we have introduced a new version of new quasi lindley distribution known as the length-biased weighted new quasi lindley distribution (LBWNQLD). Length biased distribution is a special case of weighted distribution. The different structural properties of the newly proposed distribution are derived and the model parameters are estimated by using the method of maximum likelihood estimation and also the Fisher’s information matrix have been discussed. Finally, applications to real life two data sets are presented for illustration.

The inverse power Lindley distribution

2016 ◽  
Vol 46 (8) ◽  
pp. 6308-6323 ◽  
Author(s):  
Kelly Vanessa Parede Barco ◽  
Josmar Mazucheli ◽  
Vanderly Janeiro
Keyword(s):  
Inverse Power ◽  
Lindley Distribution ◽  
Power Lindley Distribution
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