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Direct Likelihood Approximations for Generalized Linear Mixed Models: An Adaptive Approach - Basheer Ahmad
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Basheer Ahmad:

Direct Likelihood Approximations for Generalized Linear Mixed Models: An Adaptive Approach - Taschenbuch

ISBN: 3639286936

[SR: 11850983], Paperback, [EAN: 9783639286939], VDM Verlag Dr. Müller, VDM Verlag Dr. Müller, Book, [PU: VDM Verlag Dr. Müller], VDM Verlag Dr. Müller, 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., 13983, Probability & Statistics, 226699, Applied, 13884, Mathematics, 75, Science & Math, 1000, Subjects, 283155, Books, 491548, Statistics, 468218, Mathematics, 468216, Science & Mathematics, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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

Direct Likelihood Approximations for Generalized Linear Mixed Models - Taschenbuch

2010, ISBN: 3639286936

ID: 9612496176

[EAN: 9783639286939], Neubuch, [PU: VDM Verlag Sep 2010], This item is printed on demand - Print on Demand 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. 120 pp. Englisch

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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

ISBN: 9783639286939

ID: c2ee37406619b2098d8be9cd21930e33

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 Verlag

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Direct Likelihood Approximations for Generalized Linear Mixed Models - Ahmad, Basheer
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Ahmad, Basheer:
Direct Likelihood Approximations for Generalized Linear Mixed Models - Taschenbuch

2010, ISBN: 9783639286939

[ED: Softcover], [PU: Vdm Verlag Dr. Müller], 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.2010. 120 S.Versandfertig in 3-5 Tagen, [SC: 0.00]

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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

ISBN: 9783639286939

ID: 921cc0c22af93d29e11df8301c191126

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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Direct Likelihood Approximations for Generalized Linear Mixed Models
Autor:

Ahmad, Basheer

Titel:

Direct Likelihood Approximations for Generalized Linear Mixed Models

ISBN-Nummer:

3639286936

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.

Detailangaben zum Buch - Direct Likelihood Approximations for Generalized Linear Mixed Models


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 18.07.2016 15:36:14
ISBN/EAN: 3639286936

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

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