Generalized Estimating Equations [recurso electrónico] / by Andreas Ziegler.

Por: Ziegler, Andreas [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Lecture Notes in Statistics ; 204Editor: New York, NY : Springer New York, 2011Descripción: XV, 144p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781461404996Tema(s): Statistics | Mathematical statistics | Statistics | Statistical Theory and MethodsFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 519.5 Clasificación LoC:QA276-280Recursos en línea: Libro electrónicoTexto
Contenidos:
The linear exponential family -- The quadratic exponential family -- Generalized linear models -- Maximum likelihood method -- Quasi maximum likelihood method -- Pseudo maximum likelihood method based on the linear exponential family -- Quasi generalized pseudo maximum likelihood method based on the linear exponential family -- Algorithms for solving the generalized estimating equations and the relation to the jack-knife estimator of variance -- Pseudo maximum likelihood estimation based on the quadratic exponential family -- Generalized method of moment estimation.
En: Springer eBooksResumen: Generalized estimating equations have become increasingly popular in biometrical, econometrical, and psychometrical applications because they overcome the classical assumptions of statistics, i.e. independence and normality, which are too restrictive for many problems. Therefore, the main goal of this book is to give a systematic presentation of the original generalized estimating equations (GEE) and some of its further developments. Subsequently, the emphasis is put on the unification of various GEE approaches. This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM). The author details the statistical foundation of the GEE approach using more general estimation techniques. The book could therefore be used as basis for a course to graduate students in statistics, biostatistics, or econometrics, and will be useful to practitioners in the same fields.
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos QA276 -280 (Browse shelf(Abre debajo)) 1 No para préstamo 372430-2001

The linear exponential family -- The quadratic exponential family -- Generalized linear models -- Maximum likelihood method -- Quasi maximum likelihood method -- Pseudo maximum likelihood method based on the linear exponential family -- Quasi generalized pseudo maximum likelihood method based on the linear exponential family -- Algorithms for solving the generalized estimating equations and the relation to the jack-knife estimator of variance -- Pseudo maximum likelihood estimation based on the quadratic exponential family -- Generalized method of moment estimation.

Generalized estimating equations have become increasingly popular in biometrical, econometrical, and psychometrical applications because they overcome the classical assumptions of statistics, i.e. independence and normality, which are too restrictive for many problems. Therefore, the main goal of this book is to give a systematic presentation of the original generalized estimating equations (GEE) and some of its further developments. Subsequently, the emphasis is put on the unification of various GEE approaches. This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM). The author details the statistical foundation of the GEE approach using more general estimation techniques. The book could therefore be used as basis for a course to graduate students in statistics, biostatistics, or econometrics, and will be useful to practitioners in the same fields.

19

Con tecnología Koha