Holistic Generalized Linear Models
JOURNAL OF STATISTICAL SOFTWARE(2024)
摘要
Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity -inducing constraints, sign -coherence constraints and linear constraints. The R package holiglm provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art mixed -integer conic solvers, the package can reliably solve generalized linear models for Gaussian, binomial and Poisson responses with a multitude of holistic constraints. The high-level interface simplifies the constraint specification and can be used as a drop -in replacement for the stats::glm() function.
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关键词
algorithmic regression,best subset selection,conic programming,holistic con- straints,optimization,R
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