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Direct Likelihood Approximations for Generalized Linear Mixed Models - Basheer Ahmad
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ISBN: 9783639286939

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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. Bücher / Naturwissenschaften, Medizin, Informatik & Technik / Mathematik / Stochastik & Statistik, [PU: VDM Verlag Dr. Müller, Saarbrücken]

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[ED: Taschenbuch], [PU: VDM Verlag], Neuware - 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., [SC: 23.00], Neuware, gewerbliches Angebot, 220x150x7 mm, [GW: 195g]

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Direct Likelihood Approximations for Generalized Linear Mixed Models - Basheer Ahmad
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Direct Likelihood Approximations for Generalized Linear Mixed Models - neues Buch

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An Adaptive Approach 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. Bücher / Fremdsprachige Bücher / Englische Bücher 978-3-639-28693-9, VDM

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Ahmad, Basheer: Direct Likelihood Approximations for Generalized Linear Mixed Models
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An Adaptive Approach An Adaptive Approach Bücher > Wissenschaft > Mathematik, [PU: VDM Verlag Dr. Müller, Saarbrücken]

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2010, ISBN: 9783639286939

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An Adaptive Approach, Softcover, Buch, [PU: VDM Verlag]

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Direct Likelihood Approximations for Generalized Linear Mixed Models
Autor:

Ahmad, Basheer

Titel:

Direct Likelihood Approximations for Generalized Linear Mixed Models

ISBN-Nummer:

9783639286939

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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EAN (ISBN-13): 9783639286939
ISBN (ISBN-10): 3639286936
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: VDM Verlag Dr. Mueller Akt.ges.&Co.KG

Buch in der Datenbank seit 20.03.2007 13:35:06
Buch zuletzt gefunden am 08.03.2017 19:03:42
ISBN/EAN: 9783639286939

ISBN - alternative Schreibweisen:
3-639-28693-6, 978-3-639-28693-9

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