Survival estimation in xgamma distribution under progressively type-II right censored scheme

Sen, Subhradev; Chandra, Navin; Maiti, Sudhansu S.

doi:10.3233/mas-180423

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…Another generalization of XG distribution has been provided by Sen et al (2018a) on the basis of a special mixture of exponential and gamma distributions. Parameter estimation of XG distribution under the progressively type-II censored sample has been studied by Sen et al (2018b) by using different methods. The transmuted-XG distribution has been studied by Biçer (2019).…”

Section: Introductionmentioning

confidence: 99%

The New Extension of Inverse Weibull Distribution with Applications of Medicine Data

إبراهيم¹,

Almetwally

2021

المجلة العلمیة للدراسات والبحوث المالیة والتجاریة

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Section: Introductionmentioning

confidence: 99%

The New Extension of Inverse Weibull Distribution with Applications of Medicine Data

إبراهيم¹,

Almetwally

2021

المجلة العلمیة للدراسات والبحوث المالیة والتجاریة

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“…For more better flexibility and ease of application, few extensions of xgamma distribution are been proposed in the literature (see . However, the xgamma random variables are stochastically larger than those of Lindley (see Sen et al, 2018), both the distributions are the special finite mixtures of exponential and gamma distributions and both can effectively be utilized in analyzing positively skewed data sets.…”

Section: Introductionmentioning

confidence: 99%

On Discrimination Between the Lindley and xgamma Distributions

2020

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For a given data set the problem of selecting either Lindley or xgamma distribution with unknown parameter is investigated in this article. Both these distributions can be used quite effectively for analyzing skewed non-negative data and in modeling time-to-event data sets. We have used the ratio of the maximized likelihoods in choosing between the Lindley and xgamma distributions. Asymptotic distributions of the ratio of the maximized likelihoods are obtained and those are utilized to determine the minimum sample size required to discriminate between these two distributions for user specified probability of correct selection and tolerance limit.For last one decade, Lindley distribution (Lindley, 1958) has drawn attention of the researchers for modeling survival or reliability data sets. Its properties are explicitly studied by Ghitany et al. (2008) and is shown quite flexible in modeling time-to-event data sets. Several authors have suggested significant extensions and variations of Lindley model, see for example,

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“…Recently, [29] introduced and studied a new life distribution namely, xgamma distribution which has a single parameter. The xgamma distribution, with the help of the simplicity of its density function and lucrative properties, serves as a potential life distribution (see [27] for more details). A two-parameter extension of xgamma distribution, called quasi-xgamma distribution ( [26]) resembles with xgamma distribution with respect to several distributional and survival and/or reliability properties.…”

Section: Introductionmentioning

confidence: 99%

The quasi xgamma-geometric distribution with application in medicine

Sen

Afify

Al-Mofleh

et al. 2019

Filomat

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In this paper, a new probability distribution, which is synthesized based on the quasi xgamma [26] and geometric distributions, is proposed and studied. The proposed distribution so synthesized is basically a family of positively skewed probability distributions and possesses increasing and decreasing hazard rate properties depending on the values of the unknown parameters. Different important distributional and survival and/or reliability properties are also studied. A unique characterization of the distribution is presented based on reversed hazard rate. Seven different frequentist methods of estimating unknown parameters are proposed and the methods are justified with Monte-Carlo simulation study. Flexible data generation algorithm eases the utility of the proposed model in survival and/or reliability application which is accomplished by real data analyses and by comparing with other competitive life distributions.

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Survival estimation in xgamma distribution under progressively type-II right censored scheme

Cited by 20 publications

References 13 publications

The New Extension of Inverse Weibull Distribution with Applications of Medicine Data

The New Extension of Inverse Weibull Distribution with Applications of Medicine Data

On Discrimination Between the Lindley and xgamma Distributions

The quasi xgamma-geometric distribution with application in medicine

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