Reliability estimation in Lindley distribution with progressively type II right censored sample

Krishna, Hare; Kumar, Kapil

doi:10.1016/j.matcom.2011.07.005

Cited by 94 publications

(29 citation statements)

References 10 publications

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“…Hence, equation 4is the simplified cumulative distribution function of the Lomax-G family of distributions proposed by Cordeiro et al, [25] and the corresponding probability density function of the family can be obtained from equation (4) by taking the derivative of the cdf, ( ) F x with respect to x and is obtained as:…”

Section: The Lomax Inverse Lindley Distribution (Lominlind) 21 Definmentioning

confidence: 99%

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

Ieren

Koleoso

Chama

et al. 2019

JAMCS

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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.

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Section: The Lomax Inverse Lindley Distribution (Lominlind) 21 Definmentioning

confidence: 99%

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

Ieren

Koleoso

Chama

et al. 2019

JAMCS

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“…The results showed that the maximum likelihood estimators of the scale and shape parameters can be obtained via EM algorithm based on progressive censoring. Krishna and Kumar [14] discussed the inference problems in Lindley distribution and the results shows that Lindley distribution provide good parametric fit under progressive censoring scheme for some real life situations. Also, some of the recent work on progressive censoring include but not limited to Kumar et al [15], Pak et al [16] and Rastogi and Tripathi [17].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Parameters of Poisson-Exponential Distribution Based on Progressively Type II Censoring Using the Expectation Maximization (Em) Algorithm

Gitahi¹

2017

AJTAS

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This paper considers the parameter estimation problem of test units from Poisson-Exponential distribution based on progressively type II right censoring scheme. The maximum likelihood estimators (MLEs) for Poisson-Exponential parameters are derived using Expectation Maximization (EM) algorithm. EM-algorithm is also used to obtain the estimates as well as the asymptotic variance-covariance matrix. By using the obtained variance-covariance matrix of the MLEs, the asymptotic 95% confidence interval for the parameters are constructed. Through simulation, the behavior of these estimates are studied and compared under different censoring schemes and parameter values. It is concluded that for an increasing sample size; the estimated value of the parameters converges to the true value, the variances decrease and the width of the confidence interval become narrower.

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“…There are a lot of papers study the new distributions to know: Lindley (1958), Sankaran (1970), Ghitany and Al-Mutairi (2009), Shanker et al (2013), Ghitany et al (2008), Zeghdoudi and Lazri (2016) Zeghdoudi and Nedjar (2016;2016a;2016b;2017;2017a). Recently, Krishna and Kumar (2011) used the Bayesian approach and the maximum probability for an incomplete set of data using various loss functions. On the other hand, Dey et al (1992Dey et al ( , 1999, Ibrahim et al (2012), Ali et al (2013) and Metiri et al (2016) investigated the effect of certain loss functions on Bayes Estimate and the subsequent risk for Lindley's using noninformative and informative priors.…”

Section: Introductionmentioning

confidence: 99%

On Bayesian Premium Estimators for Gamma Lindley Model under Squared Error Loss Function and Linex Loss Function

Sadoun¹,

Zeghdoudi²,

Attoui³

et al. 2017

Journal of Mathematics and Statistics

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Abstract:We consider the Gamma Lindley distribution (GaL) as the conditional distribution of (X|θ,γ), we focus on the estimation of the Bayesian premium under squared error loss function (symmetric) and linearexponential (Linex) loss function (asymmetric), using informative priors (the Gamma prior). Because of its difficulty and non-linearity, we use a numerical approximation for computing the Bayesian premium. Finally, a simulation and comparative study with varying sample sizes are given.

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Reliability estimation in Lindley distribution with progressively type II right censored sample

Cited by 94 publications

References 10 publications

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

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

Estimation of the Parameters of Poisson-Exponential Distribution Based on Progressively Type II Censoring Using the Expectation Maximization (Em) Algorithm

On Bayesian Premium Estimators for Gamma Lindley Model under Squared Error Loss Function and Linex Loss Function

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