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Scrambling Sobol' and Niederreiter–Xing Points

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Abstract:

Hybrids of equidistribution and Monte Carlo methods of integration can achieve the superior accuracy of the former while allowing the simple error estimation methods of the latter. In particular, randomized (0, m, s)-nets in basebproduce unbiased estimates of the integral, have a variance that tends to zero faster than 1/nfor any square integrable integrand and have a variance that for finitenis never more thane≐2.718 times as large as the Monte Carlo variance. Lower bounds thaneare known for special cases. Some very important (t, m, s)-nets havet>0. The widely used Sobol'sequences are of this form, as are some recent and very promising nets due to Niederreiter and Xing. Much less is known about randomized versions of these nets, especially ins>1 dimensions. This paper shows that scrambled (t, m, s)-nets enjoy the same properties as scrambled (0, m, s)-nets, except the sampling variance is guaranteed only to be belowbt[(b+1)/(b-1)]stimes the Monte Carlo variance for a least-favorable integrand and finiten.

Keywords: integration; latin hypercube; multiresolution; orthogonal array sampling; quasi-Monte Carlo; wavelets

Document Type: Research Article

Affiliations: Department of Statistics, Stanford University, Sequoia Hall, 370 Serra Mall, Stanford, California, 94305

Publication date: December 1, 1998

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