Authors Kassandra Fronczyk and Athanasios Kottas

Published On 03/21/2012 03:50 PM

Department Applied Mathematics & Statistics

Abstract We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature.

REVISED October 2, 2013

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UCSC-SOE-12-01