Evaluation of Gaussian orthant probabilities based on orthogonal projections to subspaces

• Published in: Statistics and computing Vol. 26; no. 1-2; pp. 187 - 197
• Main Author: Nomura, Noboru
• Format: Journal Article
• Language: English
• Published: New York Springer US 2016
• Subjects: Artificial Intelligence; Mathematics and Statistics; Probability and Statistics in Computer Science; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Orthant probability; Conditional distribution; Recursive integration; Polyhedral cone; Cumulative distribution function
• ISSN: 0960-3174; 1573-1375
• DOI: 10.1007/s11222-014-9487-8

In this paper, a new procedure is described for evaluating the probability that all elements of a normally distributed vector are non-negative, which is called the non-centered orthant probability. This probability is defined by a multivariate integral of the density function. The definition is simple, and this probability arises frequently in statistics because the normal distribution is prevalent. The method for evaluating this probability, however, is not obvious, because applying direct numerical integration is not practical except in low dimensional cases. In the procedure proposed in this paper, the problem is decomposed into sub-problems of lower dimension. Considering the projection onto subspaces, the solutions of the sub-problems can be shared in the evaluation of higher dimensional problems. Thus the sub-problems form a lattice structure. This reduces the computational time from a factorial order, where the interim results are not shared, to order p 2 2 p , which is faster than the procedures that have been reported in the literature.