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Nov 7, 2018 at 12:49 vote accept AlexConfused
Nov 6, 2018 at 12:41 answer added AlexConfused timeline score: 0
Sep 16, 2018 at 8:40 comment added Richard Hardy Therefore, I am not sure whether the current formulation of the question quite makes sense.
Sep 16, 2018 at 8:38 comment added Richard Hardy The regression problem you present defines an estimator based on a loss function that is being minimized. Meanwhile, maximum likelihood (ML) defines an estimator based on maximizing the likelihood. I do not think the two can ever coincide since ML would never yield a penalized solution such as this one. ML is not even an method for calculating parameter estimates, unlike quadratic solvers, gradient descent, Newton-Raphson etc. (I have a problem finding the right term for all of these; optimization methods, maybe?). ML tells you what you are looking for but not how to calculate it mechanically.
Aug 28, 2018 at 16:30 comment added jbowman This is actually related to the penalty used by P-splines, see projecteuclid.org/download/pdf_1/euclid.ss/1038425655 (Eilers and Marx, Flexible Smoothing with B-splines and Penalties).
Aug 28, 2018 at 16:15 comment added AlexConfused people.cs.vt.edu/gangwang/ccs18.pdf The mentioned paragraph on the computation is directly below equation (7)
Aug 28, 2018 at 15:59 comment added Sextus Empiricus Do you have a reference to the paper?
Aug 28, 2018 at 15:00 history tweeted twitter.com/StackStats/status/1034455737693622276
Aug 28, 2018 at 14:29 history edited AlexConfused CC BY-SA 4.0
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Aug 28, 2018 at 13:33 answer added Sextus Empiricus timeline score: 1
Aug 28, 2018 at 12:56 history edited Richard Hardy
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Aug 28, 2018 at 12:55 review First posts
Aug 28, 2018 at 15:54
Aug 28, 2018 at 12:55 history asked AlexConfused CC BY-SA 4.0