Inference for Support Vector Regression under ℓ1 Regularization
Keyword(s):
L1 Norm
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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 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.
2017 ◽
Vol 21
(6)
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pp. 1017-1025
Keyword(s):
2005 ◽
Vol 1
(1)
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pp. 51
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2016 ◽
Vol 136
(12)
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pp. 898-907
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Keyword(s):
2019 ◽
Vol 12
(1)
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pp. 16
2018 ◽
Vol 6
(9)
◽
pp. 840-843