Response surface analysis with correlated data: A nonlinear model approach

Chris Gennings, Vernon Chinchilli, Walter H. Carter

Research output: Contribution to journalArticle

29 Scopus citations


Statistical methods for fitting nonlinear functions to data generated by correlated response variates are discussed. Estimation of the model parameters is performed with an iterative two-stage scheme. The estimation procedure accommodates both within-unit and between-unit variability in fitting a response surface. Under regularity conditions the procedure yields asymptotically normal, strongly consistent estimators. If desired a patterned variance-covariance matrix can be assumed and incorporated into the model. The methods are illustrated by an analysis of data from a study of the combined effects of hepatotoxins in which between- and within-subject measurements are recorded.

Original languageEnglish (US)
Pages (from-to)805-809
Number of pages5
JournalJournal of the American Statistical Association
Issue number407
StatePublished - Jan 1 1989

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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