Abstract:
For evaluating the probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based on the Gauss-Laplace law. The latter will be considered here as an element of the newly-introduced family of (p,q) -spherical distributions. Based on a suitably-defined non-Euclidean arc-length measure on (p,q) -circles, we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly to with elliptically-contoured distributions and more general homogeneous star-shaped ones. This is demonstrated by the generalization of the Box-Muller simulation method. In passing, we prove an extension of the sector and circle number functions.
Subjects:
Gauss-exponential distribution
Gauss–Laplace distribution
stochastic vector representation
geometricmeasure representation
(p, q)-generalized polar coordinates
(p, q)-arc length
dynamic intersection proportion function
(p, q)-generalized Box–Muller simulation method
(p, q)-spherical uniform distribution
dynamic geometric disintegration