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Alternative sampling methods for estimating multivariate normal probabilities
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DOI：10.1016/S0304-4076(03)00212-4.png)
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Abstract
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We study the performance of alternative sampling methods for estimating multivariate normal probabilities through the GHK simulator. The sampling methods are randomized versions of some quasi-Monte Carlo samples (Halton, Niederreiter, Niederreiter-Xing sequences and lattice points) and some samples based on orthogonal arrays (Latin hypercube, orthogonal array and orthogonal array based Latin hypercube samples). In general, these samples turn out to have a better performance than Monte Carlo and antithetic Monte Carlo samples. Improvements over these are large for low-dimensional (4 and 10) cases and still significant for dimensions as large as 50. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
simulation
quasi-monte Carlo
(t,m,s)-net
lattice points
multinomial probit
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