If X is a k-dimensional random vector, we denote by X(i,j) the vector X with coordinates i and j deleted. If for each i, j the conditional distribution of Xi, Xj given X(i,j) = x(i,j) is classical bivariate normal for each then it is shown that X has a classical k-variate normal distribution.

If X is a k-dimensional random vector, we denote by X(i) the vector X with coordinate i deleted and by X(i,j) the vector X with coordinates i and j deleted. If for each i the conditional distribution of Xi given X(i) = x(i) is univariate normal for each x(i) [there exists]K-1 and if for each i,...

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...

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...

Two classes of k-dimensional distributions with generalized Pareto conditionals are characterized. This subsumes and extends earlier work on distributions with Pareto conditionals.