We present a group lasso procedure for generalized linear models (GLMs) and we study the properties of this estimator applied to sparse high-dimensional GLMs. Under general conditions on the ...

A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

Abstract: We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate. We analyze the ...

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