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Analyzing weather effects on airborne particulate matter with HGLM
Author(s) -
Dong Lee Yoon,
Yun Sungcheol,
Lee Youngjo
Publication year - 2003
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.612
Subject(s) - particulates , covariate , bayesian probability , bayesian inference , hierarchical database model , generalized linear model , generalized additive model , environmental science , inference , precipitation , linear model , multilevel model , econometrics , bayesian hierarchical modeling , computer science , statistics , meteorology , mathematics , data mining , geography , artificial intelligence , ecology , biology
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.