INFERENCE ON P(X < Y ) FOR EXPONENTIATED FAMILY OF DISTRIBUTIONS

Inference on R = P(X &lt; Y ) has been considered when X and Y belong to independent exponentiated family of distributions. Maximum Likelihood Estimator (MLE), Uniformly Minimum Variance Unbiased Estimator (UMVUE) and Bayes Estimator of R has been derived and compared through simulation study. Exact and approximate confidence intervals and Bayesian credible intervals have also been derived

Parameter Estimates of General Failure Rate Model: A Bayesian Approach

The failure rate function plays an important role in studying the lifetime distributions in reliability theory and life testing models. A study of the general failure rate model $r(t)=a+bt^{\theta-1}$, under squared error loss function taking $a$ and $b$ independent exponential random variables has been analyzed in the literature. In this article, we consider $a$ and $b$ not necessarily independent. The estimates of the parameters $a$ and $b$ under squared error loss, linex loss and entropy loss functions are obtained here

The xgamma Distribution: Statistical Properties and Application

A new probability distribution, the xgamma distribution, is proposed and studied. The distribution is generated as a special finite mixture of exponential and gamma distributions and hence the name proposed. Various mathematical, structural, and survival properties of the xgamma distribution are derived, and it is found that in many cases the xgamma has more flexibility than the exponential distribution. To evaluate the comparative behavior, stochastic ordering of the distribution is studied. To estimate the model parameter, the method of moment and the method of maximum likelihood estimation are proposed. A simulation algorithm to generate random samples from the xgamma distribution is indicated along with a simulation study. A real life dataset on the remission times of patients receiving an analgesic is analyzed, and it is found that the xgamma model provides better fit to the data as compared to the exponential model