Massive regression is one of the new frontiers of computational statistics. In this paper we propose a generalization of the group least angle regression method based on the differential geometrical structure of a generalized linear model specified by a fixed and known group structure of the predictors. An efficient algorithm is also proposed to compute the proposed solution curve.
Augugliaro, L., Mineo, A. (2014). An efficient algorithm to estimate the sparse group structure of an high-dimensional generalized linear model. In Proceedings of the 47th Scientific Meeting of the Italian Statistical Society.
An efficient algorithm to estimate the sparse group structure of an high-dimensional generalized linear model
AUGUGLIARO, Luigi;MINEO, Angelo
2014-01-01
Abstract
Massive regression is one of the new frontiers of computational statistics. In this paper we propose a generalization of the group least angle regression method based on the differential geometrical structure of a generalized linear model specified by a fixed and known group structure of the predictors. An efficient algorithm is also proposed to compute the proposed solution curve.
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