It is just a weighted nonlinear least squares then. If nlinfit accepts weights (the standard deviation for each data point would be the appropriate weight) then it is simple. A quick check of the help reveals this:
[BETA,R,J,COVB,MSE] = nlinfit(X,Y,MODELFUN,BETA0,OPTIONS,...,'Weights',W)
nlinfit can accept an optional parameter name/value pair that specifies
the observation weights:
'Weights' A vector of real positive weights the same size as Y,
each element of which specifies an observation weight.
Reducing the weight of an observation reduces the
influence of that observation on the fitted model.
This can also be specified as a function handle that
accepts a vector of predicted response values and
returns a vector of real positive weights as output.
Default is no weights.
Always a good idea to read the help.
