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arXiv · 1505.04008

Delete or merge regressors for linear model selection

Aleksandra Maj-Kańska·Piotr Pokarowski·Agnieszka Prochenka
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Abstract

We consider a problem of linear model selection in the presence of both continuous and categorical predictors. Feasible models consist of subsets of numerical variables and partitions of levels of factors. A new algorithm called delete or merge regressors (DMR) is presented which is a stepwise backward procedure involving ranking the predictors according to squared t-statistics and choosing the final model minimizing BIC. In the article we prove consistency of DMR when the number of predictors tends to infinity with the sample size and describe a simulation study using a pertaining R package. The results indicate significant advantage in time complexity and selection accuracy of our algorithm over Lasso-based methods described in the literature. Moreover, a version of DMR for generalized linear models is proposed.

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Aleksandra Maj-Kańska, Piotr Pokarowski, Agnieszka Prochenka. 2015-05-15. Delete or merge regressors for linear model selection. https://doi.org/10.1214/15-ejs1050

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