Könyv Direct Likelihood Approximations for Generalized Linear Mixed Models Basheer Ahmad

Direct Likelihood Approximations for Generalized Linear Mixed Models

An Adaptive Approach

Szerző: Basheer Ahmad
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: VDM Verlag
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
18 978 Ft
It is a standard approach to consider the maximum likelihood estimation procedure for the estimation...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2010
oldal
120
EAN
9783639286939
ISBN
3639286936
Enbook ID
06835835
Kiadó
Súly
186
Méretek
152 x 229 x 7

Teljes leírás

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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