This paper deals with the problem of estimating the variance based on a random sample from a normal distribution with mean [mu] and variance [sigma] = 1/[tau]. In approaching the problem from a Bayesian point of view, a natural conjugate family of priors for ([mu], [tau]) consists of [tau]...

Motivated by the observation that for a sample of size two from an exponential distribution, the largest order statistic is distributed as a convolution of two independent exponential random variables with distributions differing only in their intensity or rate parameter, a spectrum of related...

In this work we consider a power series of the form X=∑j=0∞δjZj where 0δ1 and {Zj}j≥0 is an i.i.d. sequence of random variables. We show that X is well-defined iff E[(log|Z0|)+]∞ and establish a number of properties of the distribution of X, such as continuity and closure under...

What is the distribution of the product of given powers of independent uniform (0, 1) random variables? Is this distribution useful? Is this distribution commonly used in some contexts? Is this distribution somehow related to the distribution of the product of other random variables? Are there...

If X1 and X2 are independent and identically distributed (i. i. d.) with finite variance, then (X1+X2)/[radical sign]2 has the same distribution as X1 if and only if X1 is normal with mean zero (Pólya [9]). The idea of using linear combinations of i. i. d. random variables to characterize the...

Based on inverse Gaussian random variables being transformations of skew-normal random variables, multivariate inverse Gaussian densities are obtained from appropriate multivariate skew-normal distributions. The new skew-normal distributions have some closure properties not satisfied by other...

Two new concepts of order statistics for multivariate samples are introduced. In one of the versions it turns out that not every multivariate order statistic is present in every sample. These order statistics have application in multivariate ranked set sampling and can be used to generate broad...

It is well known that full knowledge of all conditional distributions will typically serve to completely characterize a bivariate distribution. Partial knowledge will often suffice. For example, knowledge of the conditional distribution of X given Y and the conditional mean of Y given X is often...