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PROC NLIN does ordinary nonlinear least squares, rather than maximum likelihood estimation. However, if the error term is assumed to have a distribution in the exponential family (this includes binomial, poisson, normal, gamma, and inverse gaussian distributions), it has been shown that an iteratively reweighted least squares approach is equivalent to maximum likelihood estimation. For more details, see the paper by Jennrich and Moore (1975) referenced at the end of the NLIN chapter in the SAS/STAT User's Guide.

If you want maximum likelihood estimates for a linear model with a binomial error distribution, see PROC LOGISTIC, PROBIT, GENMOD, or CATMOD. For a linear model with a poisson, gamma, inverse gaussian error distribution, see PROC GENMOD. For a linear model with a normal error distribution, see PROC MIXED or PROC GENMOD. PROC PHREG and PROC LIFEREG fit survival models using maximum likelihood estimation. For a general maximum likelihood estimation procedure, see PROC NLP.

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