A new reconstruction of multivariate normal orthant probabilities. A new method is introduced for geometrically reconstructing orthant probabilities for non-singular multivariate normal distributions. Orthant probabilities are expressed in terms of those for auto-regressive sequences and an efficient method is developed for numerical approximation of the latter. The approach allows more efficient accurate evaluation of the multivariate normal cumulative distribution function than previously, for many situations where the original distribution arises from a graphical model. An implementation is available as a package for the statistical software R and an application is given to multivariate probit models.
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References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Evans, Steven N.; Rivest, Ronald L.; Stark, Philip B.: Leading the field: fortune favors the bold in Thurstonian choice models (2019)
- Nomura, Noboru: Orthant probabilities of elliptical distributions from orthogonal projections to subspaces (2019)
- Colombi, Roberto; Forcina, A.: Testing order restrictions in contingency tables (2016)
- Nomura, Noboru: Evaluation of Gaussian orthant probabilities based on orthogonal projections to subspaces (2016)
- Ridgway, James: Computation of Gaussian orthant probabilities in high dimension (2016)
- Moffa, Giusi; Kuipers, Jack: Sequential Monte Carlo EM for multivariate probit models (2014)
- Nomura, Noboru: Computation of multivariate normal probabilities with polar coordinate systems (2014)
- Cattelan, Manuela: Models for paired comparison data: a review with emphasis on dependent data (2012)
- Masarotto, Guido; Varin, Cristiano: Gaussian copula marginal regression (2012)
- Bellio, Ruggero; Grassetti, Luca: Semiparametric stochastic frontier models for clustered data (2011)
- Craig, Peter: A new reconstruction of multivariate normal orthant probabilities (2008)