Inference for Support Vector Regression under ℓ1 Regularization
- (pp. 611-15)
AbstractWe 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 that 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 that displays competitive power properties and does not depend on the choice of a tuning parameter.
CitationBai, Yuehao, Hung Ho, Guillaume A. Pouliot, and Joshua Shea. 2021. "Inference for Support Vector Regression under ℓ1 Regularization." AEA Papers and Proceedings, 111: 611-15. DOI: 10.1257/pandp.20211035
- C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models