Trying different parameterizations is often a wise with nonlinear
regression. However, I know of no general rule for finding a good one
other than to try several and try to fit a paraboloid to the sums of
squares surface in a region of the least squares solution: The best
Dear R-Help list,
I have a nonlinear least squares problem, which involves a changepoint;
at the beginning, the outcome y is constant, and after a delay, t0, y
follows a biexponential decay. I log-transform the data, to stabilize
the error variance. At time t t0, my model is
