Scaled Least Squares Estimator for GLMs in Large-Scale Problems
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 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. n >> p >> 1. We show that in GLMs with random (not necessarily Gaussian) design, the GLM coefficients are approximately propo...More
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