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Bayesian Analysis for Linearized Multi‐Stage Models in Quantal Bioassay
Author(s) -
Kuo Lynn,
Kohen Michael P.
Publication year - 1999
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/(sici)1521-4036(199903)41:1<53::aid-bimj53>3.0.co;2-g
Subject(s) - bayes factor , bayesian probability , bayes' theorem , gibbs sampling , model selection , statistics , bioassay , mathematics , selection (genetic algorithm) , sampling (signal processing) , econometrics , computer science , machine learning , biology , filter (signal processing) , computer vision , genetics
Bayesian methods for estimating dose response curves from linearized multi‐stage models in quantal bioassay are studied. A Gibbs sampling approach with data augmentation is employed to compute the Bayes estimates. In addition, estimation of the “relative additional risk” and the “risk specific dose” is studied. Model selection based on conditional predictive ordinates from cross‐validated data is developed. Model adequacy is addressed by means of a posterior predictive tail‐area test.