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Quasi-Monte Carlo for Highly Structured Generalised Response Models

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Abstract

Highly structured generalised response models, such as generalised linear mixed models and generalised linear models for time series regression, have become an indispensable vehicle for data analysis and inference in many areas of application. However, their use in practice is hindered by high-dimensional intractable integrals. Quasi-Monte Carlo (QMC) is a dynamic research area in the general problem of high-dimensional numerical integration, although its potential for statistical applications is yet to be fully explored. We survey recent research in QMC, particularly lattice rules, and report on its application to highly structured generalised response models. New challenges for QMC are identified and new methodologies are developed. QMC methods are seen to provide significant improvements compared with ordinary Monte Carlo methods.

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Authors and Affiliations

  1. School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia

    F. Y. Kuo, W. T. M. Dunsmuir, I. H. Sloan, M. P. Wand & R. S. Womersley

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  1. F. Y. Kuo
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  2. W. T. M. Dunsmuir
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  3. I. H. Sloan
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  4. M. P. Wand
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  5. R. S. Womersley
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Correspondence to W. T. M. Dunsmuir.

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Kuo, F.Y., Dunsmuir, W.T.M., Sloan, I.H. et al. Quasi-Monte Carlo for Highly Structured Generalised Response Models. Methodol Comput Appl Probab 10, 239–275 (2008). https://doi.org/10.1007/s11009-007-9045-3

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