partially linear models
This doesn't look like an R question, as I know of no pre-packaged functionality publicly available that can fit the model that Elizabeth described, and it doesn't seem like she's particularly interested in an R-based answer, either. My gut feeling is that if there is a test of significance for beta in such a model, it probably shouldn't depend upon how f() is fitted, wavelets or otherwise. I.e., any test for the linear component in a partially linear model ought to do just fine. The main difference here, from a fully linear model, is that one no longer can estimate E(y) without bias, even with the assumption that the model is correct. What gets messier still is if data-dependent smoothing/de-noising is done in estimating f(), as that opens up a whole bucket of nasty creatures. I could be off, though, so take this with a truck-load of NaCl... Andy From: Spencer Graves
I have seen no replies to this post, and I don't know
that I can
help, either. However, I wonder if you tried "RSiteSearch" with your
favorite key words and phrases? For example, I just got 107 hits for
'RSiteSearch("wavelets")'. I wonder if any of them might help you.
If you'd like further help from this list, please
submit another
post. However, before you do, I suggest you read the posting guide!
"www.R-project.org/posting-guide.html". Anecdotal evidence suggests
that posts more consistent with the guide tend to receive
quicker, more
useful replies.
Best Wishes,
spencer graves
Elizabeth Lawson wrote:
Hey, I am estiamting a partially linear model
y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets.
Has anyone heard of methods to test if the betas are
significant or to address model fit?
Thanks for any thoughts or comments. Elizabeth Lawson
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