Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 019

Lasso, knockoff and Gaussian covariates: a comparison

Laurie Davies

Given data y and k covariates xj one problem in linear regression
is to decide which if any of the covariates to include when regressing
the dependent variable y on the covariates xj . In this paper three
such methods, lasso, knockoff and Gaussian covariates are compared
using simulations and real data. The Gaussian covariate method is
based on exact probabilities which are valid for all y and xj making
it model free. Moreover the probabilities agree with those based on
the F-distribution for the standard linear model with i.i.d. Gaussian
errors. It is conceptually, mathematically and algorithmically very
simple, it is very fast and makes no use of simulations. It outperforms
lasso and knockoff in all respects by a considerable margin.


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