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Hierarchical Generalized Linear Models (hierarchical + generalized_linear_models)

Distribution by Scientific Domains
Mathematics and Statistics100%

Selected Abstracts

Analyzing weather effects on airborne particulate matter with HGLM

ENVIRONMETRICS, Issue 7 2003
Yoon Dong Lee
Abstract Particulate matter is one of the six constituent air pollutants regulated by the United States Environmental Protection Agency. In analyzing such data, Bayesian hierarchical models have often been used. In this article we propose the use of hierarchical generalized linear models, which use likelihood inference and have well developed model-checking procedures. Comparisons are made between analyses from hierarchical generalized linear models and Daniels et al.'s (2001) Bayesian models. Model-checking procedure indicates that Daniels et al.'s model can be improved by use of the log-transformation of wind speed and precipitation covariates. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Bayesian hierarchical generalized linear models for a geographical subset of recovery data

ENVIRONMETRICS, Issue 2 2002
Daniela Cocchi
Abstract The aim of this work is to check whether modifications in the length of the hunting seasons had an effect on the chance of reproduction of different species of ringed birds. We start from a national data set of ringing-recovered data on three species of game birds. Only data on birds recovered as juveniles are used. Data on recoveries are organized in a 4-way contingency table. Several generalized linear models are proposed for the counts of recovered birds. Bayesian hierarchical modeling is particularly suitable for this kind of data, for which an over-dispersion parameter can be introduced at the second level of the hierarchy. Maximum Likelihood and Bayesian solutions are computed for the different models: the Bayesian framework, in particular under an individual modeling of over-dispersion, exhibits the best fit in terms of Bayesian p -value. The results show that the modification in the length of the hunting seasons does not produce equal benefits for the three species considered. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Double hierarchical generalized linear models (with discussion)

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 2 2006
Youngjo Lee
Summary., We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy-tailed distributions to be explored, providing robust estimation against outliers. The h -likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities. [source]