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- From: hrubin@pop.stat.purdue.edu (Herman Rubin)
- Subject: Re: Rational functions: a linear model?
- Message-ID: <BvyzDy.LJC@mentor.cc.purdue.edu>
- Sender: news@mentor.cc.purdue.edu (USENET News)
- Organization: Purdue University Statistics Department
- References: <1992Oct11.153538.12821@zip.eecs.umich.edu>
- Date: Sun, 11 Oct 1992 18:29:58 GMT
- Lines: 33
-
- In article <1992Oct11.153538.12821@zip.eecs.umich.edu> ayman@eecs.umich.edu (Ayman I. Kayssi) writes:
- >I'm trying to fit data to a rational function. What is the transformation
- >that I need to make to have a linear model?
- >y = (a0 + a1*x + a2*x^2 + ... + am*x^m)/(1 + b1*x + b2*x^2 + ... + bn*x^n)
- >means that:
- >y + b1*y*x + b2*y*x^2 + ... + bn*y*x^n = a0 + a1*x + ... + am*x^m
- >or:
- >y = a0 + a1*x + ... + am*x^m + b1*(-y)*x + b2*(-y)*x^2 + ... + bn*(-y)*x^n
-
- >I tried this and I'm not getting the right answers...is this the right
- >way to do it?
-
- 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.
- There are ways that this can be handled (an INDICATION of how to do it is
- to use higher powers of x to identify the situation, and to estimate it
- as a single equation in a complete system), but this is very definitely
- not optimal. There is NO way to transform a non-linear model to a linear
- model preserving the assumptions. Figure out your assumptions, and then
- use the appropriate statistical procedures.
-
- A word of warning is appropriate. Fitting a polynomial or a rational
- function is very ill-conditioned, no matter how it is done. You may
- need not only the services of a good mathematical statistician, but
- also an excellent numerical analyst. You may very well need the numerical
- analyst even if there are no random errors; crude programs are likely to
- break down.
-
- --
- Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
- Phone: (317)494-6054
- hrubin@pop.stat.purdue.edu (Internet, bitnet)
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-