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Constained regression 2 - regression model/solver
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I have a regression model log (r(i)) = a + b * log(A(i)) where A(i) is a vector and each element is known. Log is the nature log.
I need to find out a, b, and each element of r(i) such that the sum of r(i) equals to a constant k and the sum of error, sum(square[log (r(i)) – log(P(i)])is minimized, here each element in vector P(i) is known. a and b are scalars.
What regression model/solver should I choose? How to accomplish this gooal?
Thanks!
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John D'Errico
el 15 de Mzo. de 2015
Editada: John D'Errico
el 15 de Mzo. de 2015
A virtually identical re-ask of your last question, except that in this version, the main difference is you added the Thanks! at the end.
Respuestas (1)
Torsten
el 16 de Mzo. de 2015
min: sum_{i=1}^{N} (log((a+b*log(A(i))/P(i)))^2
under the constraint
exp(a)*sum_{i=1}^{N} A(i)^b - k = 0
Use fmincon to solve for a and b.
Best wishes
Torsten.
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