[…] A previous article discusses a "Catch-22" paradox for fitting nonlinear regression models: You can't estimate the parameters until you fit the model, but you can't fit the model until you provide an initial guess for the parameters! If your initial guess for the parameters is not good enough, the nonlinear optimization algorithm that tries to maximize the loglikelihood might not converge. The previous article shows how to specify a grid of initial parameter values in PROC NLIN and PROC NLMIXED. The procedures evaluate the loglikelihood (LL) of the model on each tuple of parameters in the grid, then uses the one that has the largest loglikelihood as the initial guess for the optimization. Although we often speak about MAXIMUM likelihood estimation, many routines minimize the NEGATIVE loglikelihood. Thus, in this article, the better parameters are those that have smaller values for the negative LL. …