Approximate Inference in Generalized Linear Mixed Models
In: Journal of the American Statistical Association, Jg. 88 (1993-03-01), S. 9-25
Online
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Zugriff:
Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior weights and a scale factor. The random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components. For problems involving time series, spatial aggregation and smoothing, the dispersion may be specified in terms of a rank deficient inverse covariance matrix. Approximation of the marginal quasi-likelihood using Laplace's method leads eventually to estimating equations based on penalized quasilikelihood or PQL for the mean parameters and pseudo-likelihood for the variances. Im...
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Approximate Inference in Generalized Linear Mixed Models
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Autor/in / Beteiligte Person: | Clayton, D. G. ; Breslow, Norman E. |
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Zeitschrift: | Journal of the American Statistical Association, Jg. 88 (1993-03-01), S. 9-25 |
Veröffentlichung: | Informa UK Limited, 1993 |
Medientyp: | unknown |
ISSN: | 1537-274X (print) ; 0162-1459 (print) |
DOI: | 10.1080/01621459.1993.10594284 |
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