Penalised Euclidean distance regression

Vasiliu, Daniel and Dey, Tanujit and Dryden, Ian L. (2018) Penalised Euclidean distance regression. Stat, 7 (1). e175/1-e175/14. ISSN 2049-1573

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Abstract

A method is introduced for variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The methodology involves minimising a penalised Euclidean distance, where the penalty is the geometric mean of the $\ell_1$ and $\ell_2$ norms of the regression coefficients. This particular formulation exhibits a grouping effect, which is useful for model selection in high dimensional problems. Also, an important result is a model consistency theorem, which does not require an estimate of the noise standard deviation. An algorithm for estimation is described, which involves thresholding to obtain a sparse solution. Practical performances of variable selection and prediction are evaluated through simulation studies and the analysis of real datasets.

Item Type: Article
Keywords: Euclidean distance; grouping; penalization; prediction; regularization; sparsity; variable screening
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.1002/sta4.175
Depositing User: Dryden, Professor Ian
Date Deposited: 13 Dec 2017 13:52
Last Modified: 27 Feb 2018 11:00
URI: http://eprints.nottingham.ac.uk/id/eprint/48710

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