🚀 go-pugleaf

In article <1992Oct11.192921.22075@zip.eecs.umich.edu> ayman@eecs.umich.edu (Ayman I. Kayssi) writes:
>hrubin@pop.stat.purdue.edu (Herman Rubin) wrote:
>>In article <1992Oct11.153538.12821@zip.eecs.umich.edu> ayman@eecs.umich.edu (Ayman I. Kayssi) writes:
>>>y = a0 + a1*x + ... + am*x^m + b1*(-y)*x + b2*(-y)*x^2 + ... + bn*(-y)*x^n

>>If you are trying a regression on your transformed equation, the estimates
>>will not even be consistent because of dependent variables on the right side.


>TableCurve (from Jandel) lists the following form under *linear* equations:

>    a + c x + e x^2 + ...
>y = ---------------------
>    1 + b x + d x^2 + ...

>How do they (non-iteratively) calculate the parameters?

I am not familiar with the work; I do not even know if data is being fitted,
or a theoretical function.  A standard numerical analysis situation is the
fitting of approximations of fixed type; as infinite series, the problem 
is hopelessly unidentified.  As I have stated before, there are quite 
substantial numerical problems, although these may not be too crucial for
the degree of fit.  If the fit is to observational data, it may even be a
good idea to use ridge regression or some other Bayesian, or approximate
Bayesian, approacth.

But I cannot see any good reason why it is important, or even desirable, to
calculate non-iteratively, unless this is to be a "field" situation, with
thousands or millions of cases to be computed.
-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@pop.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!pop.stat!hrubin(UUCP)