Scaled Least Squares Estimator for GLMs in Large-Scale Problems
NIPS, pp. 3324-3332, 2016.
We study the problem of efficiently estimating the coefficients of generalized linear models (GLMs) in the large-scale setting where the number of observations $n$ is much larger than the number of predictors $p$, i.e. $ngg p gg 1$. We show that in GLMs with random (not necessarily Gaussian) design, the GLM coefficients are approximately ...More
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