Name
Abstract | ||
|---|---|---|
New formulas are derived for multivariate normal probabilities defined for hyper-rectangular probability regions. The formulas use conditioning with a sequence of bivariate normal probabilities. The result is an approximate formula for multivariate normal probabilities which uses a product of bivariate normal probabilities. The new approximation method is compared with approximation methods based on products of univariate normal probabilities, using tests with random covariance-matrix/probability-region problems for up to twenty variables. The reordering of variables is studied to improve efficiency of the new method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11222-014-9468-y | Statistics and Computing |
Keywords | Field | DocType |
Multivariate normal,Bivariate conditioning approximation,Multinomial probit probability | Matrix normal distribution,Mathematical optimization,Conditioning,Multivariate normal distribution,Statistics,Bivariate analysis,Univariate,Mathematics,Law of total probability | Journal |
Volume | Issue | ISSN |
25 | 5 | 0960-3174 |
Citations | PageRank | References |
3 | 0.62 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| giang trinh | 1 | 3 | 0.62 |
| Alan Genz | 2 | 185 | 27.16 |