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...

A k-dimensional density function is determined by certain combinations of marginal and conditional densities. The present paper identifies all possible such specifications. Gelman and Speed (1993) have treated the finite discrete case of this problem. The present paper extends their work to a...

In this article, a new methodology for obtaining a premium based on a broad class of conjugate prior distributions, assuming lognormal claims, is presented. The new class of prior distributions arise in a natural way, using the conditional specification technique introduced by Arnold, Castillo,...

Distributions with normal conditionals have biquadratic regression functions. Consequently, in contrast to classical bivariate normal distributions, their densities can be multimodal. Criteria for determining the number of modes are discussed and illustrations of representative multimodal...

A new discrete distribution depending on two parameters, [alpha]1,[alpha][not equal to]0 and 0[theta]1, is introduced in this paper. The new distribution is unimodal with a zero vertex and overdispersion (mean larger than the variance) and underdispersion (mean lower than the variance) are...

In this paper, we study reliability properties in two classes of bivariate continuous distributions based on specification of conditional hazard functions. These classes were constructed by conditioning on two different kinds of events in Arnold and Kim [6]. Several reliability properties are...