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hat matrices #2
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Wei, just tagging you here for tracking. Don't worry about it, & keep plugging that dissert 😄 |
My working notebook on the subject. Not primetime, by far, but anyone interested in digging in can see how I started. I'll also be updating that notebook as I keep working. |
I also tried the math above and did not get luck.
Instead of taking inverse of y (as a vector), I wonder if taking partial derivative of y makes sense which can also leave S alone on the right hand side. But this gives another problem that need to solve the partial derivative of the second term (S1f2+S2f1), which is a function of y. I am thinking if we could find another equation/relationship among f1,f2 and y rather than equations above, the second term might be solvable. Just some random thoughts. |
Mathematical details to come, but I think we can close this based on Hanchen's recent efforts. |
Wei's suggestion and the back and forth we had back in Marchish of 2017 was:
We know that a simple definition of a hat matrix is
\hat{y} = S y
for hat matrixS
.If
\hat{y} = \sum_j^p \hat{f}_j
, then maybe we can getS
from expanding the estimators of\hat{f}_j
, given that each is\hat{f}_j = S_j( y - \sum_{k \neq j}^p \hat{f}_k)
for process-specific hat matrixS_j
.In one line:
Immediate question I have is: what's
y^{-1}
, given it's a vector?Strategies I've looked into include:



which is just 1/y diagonalized.
inspired by the adjoint-determinant definition of the inverse
where that cross-times is a elementwise product, which is about as literal an interpretation of the factor-out logic I can see.
None of this yields a hat matrix. In most cases, the second term is larger than the first term at nearly all elements, so you end up with a hat matrix with values somewhere between -4 and 0. Then, taking the dot of that and y gives you massive too large numbers. BUT their general pattern looks sort of like the predicted values.
I'll post code here I'm using to generate these values, as well as track further ruminations.
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