A simulation-based comparison between parametric and nonparametric estimation methods in PBPK models

H. T. Banks, Y. Ma, L. K. Potter

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We compare parametric and nonparametric estimation methods in the context of PBPK modeling using simulation studies. We implement a Monte Carlo Markov Chain simulation technique in the parametric method, and a functional analytical approach to estimate the probability distribution function directly in the nonparametric method. The simulation results suggest an advantage for the parametric method when the underlying model can capture the true population distribution. On the other hand, our calculations demonstrate some advantages for a nonparametric approach since it is a more cautious (and hence safer) way to assess the distribution when one does not have sufficient knowledge to assume a population distribution form or parametrization. The parametric approach has obvious advantages when one has significant a priori information on the distributions sought, although when used in the nonparametric method, prior information can also significantly facilitate estimation.

Original languageEnglish (US)
Pages (from-to)1-26
Number of pages26
JournalJournal of Inverse and Ill-Posed Problems
Volume13
Issue number1
DOIs
StatePublished - 2005

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

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