[R] slightly OT: constrained least-squares estimation in a decomvolution model
maitra at iastate.edu
Tue May 15 05:42:32 CEST 2007
This is not strictly a R question, but more a methodology-related question.
I have the following linear model: Y = X\beta + e.
Pretty standard stuff, but additionally, X is square, symmetric circulant. So, the LS estimate for \beta is given by just deconvolving Y with the inverse of X, and can be done using 1-d discrete convolution.
Now, suppose that I also add in the constraint that some of the \beta's are zero. Is it still possible to still use the convolution property (and the fact that the whole X matrix is circulant, symmetric) in some way?
This is important in my application, because discrete convolution is what makes the LS estimate of \beta able to be computed and I have to do it several times.
Any ideas or pointers on how to handle this? Has anyone dealt with this, in R or elsewhere?
Many thanks and best wishes,
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