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Bayesian multimodel inference for dose-response studies

Environmental Toxicology and Chemistry

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Abstract

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Bayesian multimodel inference for dose-response studies
Series title:
Environmental Toxicology and Chemistry
Volume
26
Issue:
9
Year Published:
2007
Language:
English
Contributing office(s):
Patuxent Wildlife Research Center
Description:
1867-1872
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Environmental Toxicology and Chemistry
First page:
1867
Last page:
1872