Kniha Direct Likelihood Approximations for Generalized Linear Mixed Models Basheer Ahmad

Direct Likelihood Approximations for Generalized Linear Mixed Models

An Adaptive Approach

Autor: Basheer Ahmad
Jazyk: Angličtina
Väzba: Brožovaná
Vydavateľ: VDM Verlag
Dostupnosť: Skladom u dodávateľa
Odosielame za 9-15 dní
47.54
It is a standard approach to consider the maximum likelihood estimation procedure for the estimation...

Informácie o knihe

Jazyk
Angličtina
Väzba
Kniha - Brožovaná
Vydalo
2010
Stránok
120
EAN
9783639286939
ISBN
3639286936
Enbook ID
06835835
Vydavateľ
Hmotnosť
186
Rozmery
152 x 229 x 7

Kompletný popis

It is a standard approach to consider the maximum likelihood estimation procedure for the estimation of parameters in statistical modelling. The sample likelihood function has a closed form representation only if the two densities in the integrand are conjugate to each other. In case of any non- conjugate pair, no closed form representation exists. In such situations, we need to approximate the integral by making use of some numerical techniques. A first or second order Laplace approximation or the (adaptive) Gauss-Hermite quadrature method can be applied in order to get an approximative objective function. The resulting approximation of the likelihood function still needs to be numerically maximized with respect to all unknown parameters. For such a numerical maximization, all required derivatives are provided in the scope of this work. We explore the use of the (adaptive) Gauss-Hermite quadrature for Generalized Linear Mixed Models, when the conditional density of the response given the random effects is a member of the linear exponential family and the random effects are Gaussian.

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