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Split Regularized Regression

We propose an approach for fitting linear regression models that splits the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit. Our procedure works on top of a given penalized linear regression estimator (e.g., Lasso, elastic net) by fitting it to possibly overlapping groups of features, encouraging diversity among these groups to reduce the correlation of the corresponding predictions. For the case of two groups, elastic net penalty and orthogonal predictors, we give a closed form solution for the regression coefficients in each group. We establish the consistency of our method with the number of predictors possibly increasing with the sample size. An extensive simulation study and real-data applications show that in general the proposed method improves the prediction accuracy of the base estimator used in the procedure. Possible extensions to GLMs and other models are discussed. The supplemental material for this article, available online, contains the proofs of our theoretical results and the full results of our simulation study.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.62 No.3 August 2020

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 330 - 338

Bahasa

English

ISBN/ISSN

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Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

LINEAR REGRESSION
ELASTIC NET
HIGH-DIMENSIONAL DATA
SMALL SAMPLE SIZE

Info Detail Spesifik

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Pernyataan Tanggungjawab

Anthony-Alexander Christidis, Laks Lakshmanan, Ezequiel Smucler, Ruben Zamar

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