Statistical inference for the parameter of the inverse Lindley distribution based on imprecise data with simulation study

2019 ◽  
Vol 14 (4) ◽  
pp. 151-161 ◽  
Author(s):  
Iman S. Mabrouk
Keyword(s):  
Statistical Inference ◽  
Simulation Study ◽  
Imprecise Data ◽  
Lindley Distribution ◽  
Inverse Lindley Distribution

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

Statistical inference of the lifetime performance index for Lindley distribution under progressive first-failure censoring scheme applied to HPLC data

2018 ◽  
Vol 11 (05) ◽  
pp. 1850073 ◽  
Author(s):  
W. A. Hassanein
Keyword(s):  
Statistical Inference ◽  
Performance Index ◽  
Organ Transplant ◽  
Real Life ◽  
Organ Transplant Recipient ◽  
Lindley Distribution ◽  
Lifetime Performance ◽  
Censored Sample ◽  
Censoring Scheme ◽  
Hplc Data

This paper is devoted to the construct of the maximum likelihood estimator of the lifetime performance index based on first-failure progressive right type II censored sample for Lindley distribution. Statistical inference for assessing the lifetime performance of the items is performed. Finally, two examples are given, one of them considers a real life application of blood samples from organ transplant recipient using the liquid chromatography (HPLC) data and the other is a simulated example to illustrate the proposed statistical procedure.

scholarly journals Overlap coefficients based on Kullback-Leibler divergence: Exponential populations case

2017 ◽  
Vol 6 (4) ◽  
pp. 135
Author(s):  
Hamza Dhaker ◽  
Papa Ngom ◽  
Malick Mbodj
Keyword(s):  
Statistical Inference ◽  
Mean Square Error ◽  
Confidence Intervals ◽  
Taylor Series ◽  
Simulation Study ◽  
Invariance Property ◽  
Mean Square ◽  
Taylor Series Approximation ◽  
Leibler Divergence ◽  
Exponential Populations

This article is devoted to the study of overlap measures of densities of two exponential populations. Various Overlapping Coefficients, namely: Matusita’s measure ρ, Morisita’s measure λ and Weitzman’s measure ∆. A new overlap measure Λ based on Kullback-Leibler measure is proposed. The invariance property and a method of statistical inference of these coefficients also are presented. Taylor series approximation are used to construct confidence intervals for the overlap measures. The bias and mean square error properties of the estimators are studied through a simulation study.

scholarly journals Modified Slash Lindley Distribution

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jimmy Reyes ◽  
Osvaldo Venegas ◽  
Héctor W. Gómez
Keyword(s):  
Maximum Likelihood ◽  
Simulation Study ◽  
Maximum Likelihood Estimators ◽  
Lindley Distribution ◽  
Basic Properties ◽  
Moment Estimators ◽  
Proposed Model ◽  
Distribution Moment ◽  
Flexible Models ◽  
New Distribution

In this paper we introduce a new distribution, called the modified slash Lindley distribution, which can be seen as an extension of the Lindley distribution. We show that this new distribution provides more flexibility in terms of kurtosis and skewness than the Lindley distribution. We derive moments and some basic properties for the new distribution. Moment estimators and maximum likelihood estimators are calculated using numerical procedures. We carry out a simulation study for the maximum likelihood estimators. A fit of the proposed model indicates good performance when compared with other less flexible models.

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 Kumaraswamy Inverse Lindley Distribution with Stress-Strength Reliability

2020 ◽  
Vol 6 (3) ◽  
pp. 255-264
Author(s):  
Saeed E. HEMEDA ◽  
Sukanta PRAMANİK ◽  
Sudhansu S MAİTİ
Keyword(s):  
Lindley Distribution ◽  
Inverse Lindley Distribution
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