Phil Sherrod <[EMAIL PROTECTED]> wrote:
>> : This isn't a "simple linear regression" problem. It is a nonlinear
>> : regression problem. There are a number of nonlinear regression programs
>> : that can solve your problem for a and b. Here is such a program that I
>> ran
>> : through my NLREG program (http://www.nlreg.com)
>On 27-Apr-2004, Michael Hochster <[EMAIL PROTECTED]> wrote:
>> Yes, it is a simple linear regression problem: ordinary regression
>> of y on e^-x. As the author of regression software, you should know
>> better.
Phil Sherrod <[EMAIL PROTECTED]> wrote:
>I agree, by transforming the input variables this function is easily
>converted to a linear regression. But it can be handled more easily and
>properly as a nonlienar regression where no transformations are required.
?? A linear regression is easier than a nonlinear regression,
in general. For example, sometimes nonlinear regressions have multiple
local optima. A linear regression has a single answer (except when
there is perfect collinearity, which should be detected automatically).
>Remember that fitting a function to a transformed independent variable does
>not always yield the same fitting parameter results as fitting the function
>to the non-transformed input -- minimizing the sum of squared deviations for
>X is not the same as log(X) or sin(X). The difference can be significant.
What you are saying here only makes sense if you believe there is
some type of measurement error in X. If X is measured without error,
then all you minimize is the sum of squared deviations in *Y*, not *X*.
If X is measured with error, you will need additional information to
identify the model. Namely, some information on the variance or relative
variance of the errors in X. I didn't see any such suggestion or
information in the original post.
Clint Cummins
(TSP International)
.
.
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