Hi
I am attempting to convert my simple weighted regressions (produced using the
weights argument in lm) to a constrained regression where the coefficients sum
to 1. I understand that I can do this using solve.qp and I have spent time
reading the archives to understand how this is done, but I am unable to find an
example of where the constraints were introduced in a weighted regression.
I see that solve.qp will find the solution to min{(y-bx)^2} but can it be used
for min{w((y-bx)^2)}, and how would I do this?
Thanks in advance
Lewis
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