A pivotal transform for the high-dimensional location-scale model

Fuente: arXiv
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Main Authors: van de Geer, Sara, Sardy, Sylvain, van Cutsem, Maximę
Format: Preprint
Published: 2025
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author van de Geer, Sara
Sardy, Sylvain
van Cutsem, Maximę
author_facet van de Geer, Sara
Sardy, Sylvain
van Cutsem, Maximę
contents We study the high-dimensional linear model with noise distribution known up to a scale parameter. With an $\ell_1$-penalty on the regression coefficients, we show that a transformation of the log-likelihood allows for a choice of the tuning parameter not depending on the scale parameter. This transformation is a generalization of the square root Lasso for quadratic loss. The tuning parameter can asymptotically be taken at the detection edge. We establish an oracle inequality, variable selection and asymptotic efficiency of the estimator of the scale parameter and the intercept. The examples include Subbotin distributions and the Gumbel distribution.
format Preprint
id arxiv_https___arxiv_org_abs_2512_18705
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A pivotal transform for the high-dimensional location-scale model
van de Geer, Sara
Sardy, Sylvain
van Cutsem, Maximę
Statistics Theory
62J07
We study the high-dimensional linear model with noise distribution known up to a scale parameter. With an $\ell_1$-penalty on the regression coefficients, we show that a transformation of the log-likelihood allows for a choice of the tuning parameter not depending on the scale parameter. This transformation is a generalization of the square root Lasso for quadratic loss. The tuning parameter can asymptotically be taken at the detection edge. We establish an oracle inequality, variable selection and asymptotic efficiency of the estimator of the scale parameter and the intercept. The examples include Subbotin distributions and the Gumbel distribution.
title A pivotal transform for the high-dimensional location-scale model
topic Statistics Theory
62J07
url https://arxiv.org/abs/2512.18705