Penalised Euclidean distance regression

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Penalised Euclidean distance regression

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 $\ell_1$ an...

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Bibliographic Details
Main Authors:	Vasiliu, Daniel, Dey, Tanujit, Dryden, Ian L.
Format:	Article
Published:	Wiley 2018
Subjects:	Euclidean distance; grouping; penalization; prediction; regularization; sparsity; variable screening
Online Access:	https://eprints.nottingham.ac.uk/48710/

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