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Nonparametric Bayesian Methods for Benchmark Dose Estimation
Author(s) -
Guha Nilabja,
Roy Anindya,
Kopylev Leonid,
Fox John,
Spassova Maria,
White Paul
Publication year - 2013
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12004
Subject(s) - nonparametric statistics , benchmark (surveying) , frequentist inference , context (archaeology) , bayesian probability , parametric statistics , computer science , parametric model , estimation , data mining , econometrics , statistics , machine learning , bayesian inference , mathematics , artificial intelligence , engineering , biology , paleontology , geodesy , systems engineering , geography
The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose‐response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model‐averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.