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Journal ArticleDOI

Approximate inference in generalized linear mixed models

TLDR
In this paper, generalized linear mixed models (GLMM) are used to estimate the marginal quasi-likelihood for the mean parameters and the conditional variance for the variances, and the dispersion matrix is specified in terms of a rank deficient inverse covariance matrix.
Abstract
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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Citations
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Journal ArticleDOI

Alternative approaches to the analysis of binary and categorical repeated measurements.

TL;DR: A number of different approaches to the analysis of repeated binary and categorical data are described, illustrated, and compared, including empirical generalized least squares and generalized estimating equations as well as traditional log-linear modeling methods.
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Random effects logistic models for analysing efficacy of a longitudinal randomized treatment with non-adherence.

TL;DR: A random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non-adherence and longitudinal binary outcomes and an extension of Nagelkerke et al.'s instrumental variables approximation for cross-sectional binary outcomes is presented.
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Winter habitat use by red and roe deer in pine-dominated forest

TL;DR: It was concluded that, in hunted deer populations, the presence of cover is important even in areas lacking large predators, and introduction of forest understories into mature pine forests should thus be promoted in big game management.
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A Multilevel Analysis of the Relationship between Labor Surplus and Pretrial Incarceration

TL;DR: In this paper, the authors analyzed the interplay between the employment status of male criminal defendants charged with burglary and armed robbery and the unemployment rate on pretrial incarceration and found that in cities with high unemployment, unemployed criminal defendants will be more likely to be incarcerated before trial.
References
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Book

Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI

Longitudinal data analysis using generalized linear models

TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.