Estimating response modeling methodology models

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


An advanced review of Response Modeling Methodology (RMM) has recently summarized RMM core philosophy, modeling approach, and allied statistical expressions. This focus article complements the earlier review by presenting a stepby-step guide to estimating RMM models. The estimation procedure comprises two stages: first the median is estimated and then the rest of the RMM parameters are estimated. Three estimation procedures are presented for the latter stage: maximum likelihood, two-moment matching, and nonlinear quantile regression. The three estimation methods, as applied to RMM, are first expounded and then demonstrated via a numerical example, using Monte-Carlo simulated data from a γ distribution and an L4 orthogonal array design. Comparisons with generalized linear modeling and estimation, assuming γ distribution (correctly) and inverse Gaussian distribution (incorrectly), are given. A brief introduction to RMM is also provided.

Original languageEnglish
Pages (from-to)323-333
Number of pages11
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number3
StatePublished - 1 May 2012


  • Moment matching
  • Quantile regression
  • Response Modeling Methodology
  • Statistical modeling

ASJC Scopus subject areas

  • Statistics and Probability


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