Inference for Support Vector Regression under ℓ1Regularization

Yuehao Bai, Hung Ho, Guillaume A. Pouliot, Joshua Shea

Research output: Contribution to journalArticlepeer-review

Abstract

We provide large sample distribution theory for support vector regression (SVR) with l1-norm, along with error bars for the SVR regression coefficients. Although a classical Wald confidence interval obtains from our theory, its implementation inherently depends on the choice of a tuning parameter which scales the variance estimate and thus the width of the error bars. We address this shortcoming by further proposing an alternative large sample inference method based on the inversion of a novel test statistic which displays competitive power properties and does not depend on the choice of a tuning parameter.
Original languageEnglish (US)
Pages (from-to)611-615
JournalAEA Papers and Proceedings
Volume111
DOIs
StatePublished - May 1 2021
Externally publishedYes

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