Response Surface Analysis with Correlated Data: A Nonlinear Model Approach

Abstract

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.