2018•Unpublished venueOpen access

A Quasi Gamma distribution

Rama Shanker, Kamlesh Kumar Shukla, Shambhu, R. Shanker

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Abstract

In this paper, a two-parameter quasi gamma distribution which includes one parameter quasi exponential distribution has been proposed and studied. Its moments have been obtained and it has been found that the first four even order moments about origin of the proposed distribution are the first four moments about origin of the gamma distribution. Its survival function, hazard rate function, mean residual life function and stochastic ordering have been discussed. Estimation of parameters has been discussed using both the method of moments and the method of maximum likelihood. Application of the distribution has been explained with a real lifetime data from engineering.

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What this paper is about

In this paper, a two-parameter quasi gamma distribution which includes one parameter quasi exponential distribution has been proposed and studied. Its moments have been obtained and it has been found that the first four even order moments about origin of the proposed distribution are the first four moments about origin of the gamma distribution. Its survival function, hazard rate function, mean residual life function and stochastic ordering have been discussed. Estimation of parameters has been discussed using both the method of moments and the method of maximum likelihood. Application of the distribution has been explained with a real lifetime data from engineering.

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Available abstract

In this paper, a two-parameter quasi gamma distribution which includes one parameter quasi exponential distribution has been proposed and studied. Its moments have been obtained and it has been found that the first four even order moments about origin of the proposed distribution are the first four moments about origin of the gamma distribution. Its survival function, hazard rate function, mean residual life function and stochastic ordering have been discussed. Estimation of parameters has been discussed using both the method of moments and the method of maximum likelihood. Application of the distribution has been explained with a real lifetime data from engineering.

Key concepts: Gamma distribution, Generalized gamma distribution, Mathematics, Exponential distribution, Inverse-gamma distribution, Distribution (mathematics), Log-Cauchy distribution, Generalized integer gamma distribution

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