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

Rabi Bhattacharya, Lizhen Lin

Research output: Contribution to journalArticle

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)144-163
Number of pages20
JournalSankhya: The Indian Journal of Statistics
Volume73
Issue number1
DOIs
StatePublished - May 2011

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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