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Showing Abstract of Gauss-Hermite quadrature: numerical or statistical method?

 
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[ Abstract Viewed: | Pages: 7 ]

Title

Gauss-Hermite quadrature: numerical or statistical method?

Topic: Published Year: 1385
Presentation:
Published in:

[ 8th Iranian Statistics Conference ]

Original Language: English Full Text Size: Not Available

 

Abstract of the Article

 

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Download This article in PDF format Gauss-Hermite quadrature: numerical or statistical method?

 

Author:

[ Vahid Partove Ni ] - Institute of Mathematics, Ecole Polytechnique Federal de Lausanne

 

Abstract:

Gauss-Hermite Quadrature (GHQ) is often used for numerical approximation of integrals with Gaussian kernels. In generalized linear mixed models random effects are assumed to have Gaussian distributions, but often the marginal likelihood, In addition to Monte Carlo methods, first or second order Taylor expansion, Laplace approximation or GHQ are feasible tools for numerical evaluation of the integrals. In this paper we review the key ideas of GHQ. Nonparametric Maximum Likelihood (NPML) estimation is shown to be a flexible version of GHQ. A binary nested random effects model is fitted to a real data set using GHQ.

 

Keywords:

Function Interpolation; Generalized Linear Mixed Model; Hermite Polynomial; Distribution; Nonparametric Maximum Likelihood.

 

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