Response surface analysis with correlated data: A nonlinear model approach

Chris Gennings, Vernon Chinchilli, Walter H. Carter

Research output: Contribution to journalArticlepeer-review

31 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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