Title: Numerical integration in logistic-normal models
Authors: Gonz├ílez, Jorge ×
Tuerlinckx, Francis
De Boeck, Paul
Cools, Ronald #
Issue Date: Dec-2006
Publisher: North-Holland Pub. Co.
Series Title: Computational Statistics & Data Analysis vol:51 issue:3 pages:1535-1548
Abstract: Marginal maximum likelihood estimation is commonly used to estimate logistic-normal models. In this approach, the contribution of random effects to the likelihood is represented as an intractable integral over their distribution. Thus, numerical methods such as Gauss-Hermite quadrature (GH) are needed. However, as the dimensionality increases, the number of quadrature points becomes rapidly too high. A possible solution can be found among the Quasi-Monte Carlo (QMC) methods, because these techniques yield quite good approximations for high-dimensional integrals with a much lower number of points, chosen for their optimal location. A comparison between three integration methods for logistic-normal models: GH, QMC, and full Monte Carlo integration (MC) is presented. It turns out that, under certain conditions, the QMC and MC method perform better than the GH in terms of accuracy and computing time.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
Quantitative Psychology and Individual Differences
NUMA, Numerical Analysis and Applied Mathematics Section
× corresponding author
# (joint) last author

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