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In probability theory and statistics, the xgamma distribution is continuous probability distribution (introduced by Sen et al. in 2016 [1]). This distribution is obtained as a special finite mixture of exponential and gamma distributions. This distribution is successfully used in modelling time-to-event or lifetime data sets coming from diverse fields.
Exponential distribution with parameter θ and gamma distribution with scale parameter θ and shape parameter 3 are mixed mixing proportions, and , respectively, to obtain the density form of the distribution.
Here θ > 0 is the parameter of the distribution, often called the shape parameter. The distribution is supported on the interval [0, ∞). If a random variableX has this distribution, we write X ~ XG(θ).
Mean residual life (MRL) function related to a life time probability distribution is an important characteristic useful in survival analysis and reliability engineering.
The MRL function for xgamma distribution is given by
Let be n observations on a random sample of size n drawn from xgamma distribution. Then, the likelihood function is given by
The log-likelihood function is obtained as
To obtain maximum likelihood estimator (MLE) of , (say), one can maximize the log-likelihood equation directly with respect to or can solve the non-linear equation,
It is seen that
cannot be solved analytically and hence numerical iteration technique, such as, Newton-Raphson algorithm is applied to solve.
The initial solution for such an iteration can be taken as Using this initial solution, we have,
for the ith iteration.
one chooses such that .
Given a random sample of size n from the xgamma distribution, the moment estimator for the parameter of xgamma distribution is obtained as follows.
Equate sample mean, with first order moment about origin to get
which provides a quadratic equation in as
Solving it, one gets the moment estimator, (say), of as