Sankhya B

, 73:144

Nonparametric benchmark analysis in risk assessment: a comparative study by simulation and data analysis

Authors

    • Department of MathematicsThe University of Arizona
  • Lizhen Lin
    • Department of MathematicsThe University of Arizona
Article

DOI: 10.1007/s13571-011-0019-7

Cite this article as:
Bhattacharya, R. & Lin, L. Sankhya B (2011) 73: 144. doi:10.1007/s13571-011-0019-7

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, Stat Probab Lett 80:1947–1953, 2010). It is compared with three other methods, including the leading kernel-based method, called DNP, due to Dette et al. (J Am Stat Assoc 100:503–510, 2005) and Dette and Scheder (J Stat Comput Simul 80(5):527–544, 2010). In simulation studies, the present method, termed NAM, outperforms the DNP in the majority of cases considered, although both methods generally do well. In small samples, NAM and DNP both outperform the MLE.

Keywords

Monotone dose-response curve estimation Effective dosage Nonparametric method Pool-adjacent-violators algorithm Mean integrated squared error Confidence interval Bootstrap

Copyright information

© Indian Statistical Institute 2011