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- Path: sparky!uunet!haven.umd.edu!mimsy!afterlife!adm!aos!celms
- From: celms@vim.brl.mil (Dr. Aivars Celmins )
- Newsgroups: sci.math.stat
- Subject: Re: Non-linear Regression in Chemistry
- Message-ID: <1004@aos.brl.mil>
- Date: 10 Sep 92 17:44:20 GMT
- References: <1992Sep3.064230.42744@kuhub.cc.ukans.edu>
- Sender: news@aos.brl.mil
- Organization: U.S. Army Ballistic Research Laboratory, APG, MD.
- Lines: 34
- Nntp-Posting-Host: vim.brl.mil
-
- In article <1992Sep3.064230.42744@kuhub.cc.ukans.edu> jeff@kuhub.cc.ukans.edu (Jeff Bangert) writes:
- >A chemist has asked me to solve a problem: she has data and a model
- >which is to be fit by 'least squares'. It looks like non-linear
- >regression, except that:
- >
- > 1. the model has two equations
- > 2. both are non-linear
- > 3. there are parameters common to the two equations.
- >
- >I would like to know:
- >
- > 1. is there a 'standard' method for solving this problem?
- > 2. is there literature in stat or chemistry which I could read?
- >
-
- The problem is to minimize the properly weighted sum of squares of the
- corrections ( residuals) of the *observations* (not the defects of the
- equations!) subject to the constraints given by the model equation
- system. This leads to a constrained leaast-squares minimization.
-
- The minimization (least squares with model equations in form of simultaneous
- nonlinear equations) is discussed and an algorithm for the solution
- proposed in
-
-
- A. Celmins, Least-Squares Optimization with Implicit Model Functions, in
- "Mathematical Programming with Data Perturbations II", Anthony V. Fiacco,
- Ed., Marcel Dekker, 1983, pp. 131-152.
-
-
- Aivars Celmins
- celms@brl.mil
- (410)-278-6986
-
-