English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we are interested in parameters estimation of linear model whennumber of parameters increases with sample size. Without any assumption aboutmoments of the model error, we propose and study the seamless $L_0$ quantileestimator. For this estimator we first give the convergence rate. Afterwards,we prove that it correctly distinguishes between zero and nonzero parametersand that the estimators of the nonzero parameters are asymptotically normal. Aconsistent BIC criterion to select the tuning parameters is given.

Model selection in high-dimensional quantile regression with seamless $L_0$ penalty