Optimal design theory is developed for an inverse Gaussian regression model in which the reciprocal mean is a polynomial function of a concomitant variable. Comparisons are made to the traditional normal theory optimal designs for linear and nonlinear regression.

Bayesian analysis of system failure data from engineering applications under a competing risks framework is considered when the cause of failure may not have been exactly identified but has only been narrowed down to a subset of all potential risks. In statistical literature, such data are...

The linearity (or lack thereof) of association between sample mean and sample variance is explored in this note with the intent of providing new insights. Of particular interest is a well-known inequality involving the measures of skewness and kurtosis that is derived as a consequence of an...

This paper deals with the Bayesian inference of the linear hazard rate (LHR) distribution under a progressively censoring scheme. A unified treatment of both Type I and Type II censoring is presented under independent gamma priors for the parameters, that yields the posteriors as mixtures of...

In single-shot expensive tests of a destructive nature, an inverse sampling scheme is often pursued in order to use the available resources efficiently. This is particularly relevant for evaluating reliabilities for systems that are subjected to test-analyze-and-fix programs at successive...