Using random subspace method for prediction and variable importance assessment in linear regression

Jan Mielniczuk and Paweł Teisseyre

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 725-742

Abstract: A random subset method (RSM) with a new weighting scheme is proposed and investigated for linear regression with a large number of features. Weights of variables are defined as averages of squared values of pertaining t-statistics over fitted models with randomly chosen features. It is argued that such weighting is advisable as it incorporates two factors: a measure of importance of the variable within the considered model and a measure of goodness-of-fit of the model itself. Asymptotic weights assigned by such a scheme are determined as well as assumptions under which the method leads to consistent choice of significant variables in the model. Numerical experiments indicate that the proposed method behaves promisingly when its prediction errors are compared with errors of penalty-based methods such as the lasso and it has much smaller false discovery rate than the other methods considered.

Keywords: Random subspace method; High-dimensional model selection; Prediction; Variable importance; Positive selection rate; False discovery rate (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:725-742

DOI: 10.1016/j.csda.2012.09.018

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