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The yaglm package aims to make the broader ecosystem of modern generalized linear models accessible to data analysts and researchers. This ecosystem encompasses a range of loss functions (e.g. linear, logistic, quantile regression), constraints (e.g.
Externí odkaz:
http://arxiv.org/abs/2110.05567
Autor:
van de Geer, Sara
We study logistic regression with total variation penalty on the canonical parameter and show that the resulting estimator satisfies a sharp oracle inequality: the excess risk of the estimator is adaptive to the number of jumps of the underlying sign
Externí odkaz:
http://arxiv.org/abs/2003.02678
We consider the recovery of regression coefficients, denoted by $\boldsymbol{\beta}_0$, for a single index model (SIM) relating a binary outcome $Y$ to a set of possibly high dimensional covariates $\boldsymbol{X}$, based on a large but 'unlabeled' d
Externí odkaz:
http://arxiv.org/abs/1701.05230