title: NONPARAMETRIC BENCHMARK ANALYSIS IN RISK ASSESSMENT: A COMPARATIVE STUDY BY SIMULATION AND DATA ANALYSIS.
abstract: We consider the finite sample performance of a new nonparametric method for bioassay and benchmark analysis in risk assessment, which averages isotonic MLEs based on disjoint subgroups of dosages, and whose asymptotic behavior is essentially optimal (Bhattacharya and Lin (2010)). It is compared with three other methods, including the leading kernel-based method, calledDNP, due to Dette et al. (2005) and Dette and Scheder (2010). In simulation studies, the present method, termedNAM, outperforms theDNPin the majority of cases considered, although both methods generally do well. In small samples, NAM and DNP both outperform the MLE.
authors.affiliation: Department of Mathematics, The University of Arizona, Tucson, AZ, 85721, USA.
authors.forename: Rabi
authors.initials: R
authors.lastname: Bhattacharya
authors.forename: Lizhen
authors.initials: L
authors.lastname: Lin
keywords: Monotone dose-response curve estimation; bootstrap; confidence interval; effective dosage; mean integrated squared error; nonparametric method; pool-adjacent-violators algorithm