pp 1–24 | Cite as

Prediction error bounds for linear regression with the TREX

  • Jacob Bien
  • Irina Gaynanova
  • Johannes Lederer
  • Christian L. Müller
Original Paper


The TREX is a recently introduced approach to sparse linear regression. In contrast to most well-known approaches to penalized regression, the TREX can be formulated without the use of tuning parameters. In this paper, we establish the first known prediction error bounds for the TREX. Additionally, we introduce extensions of the TREX to a more general class of penalties, and we provide a bound on the prediction error in this generalized setting. These results deepen the understanding of the TREX from a theoretical perspective and provide new insights into penalized regression in general.


TREX High-dimensional regression Tuning parameters Oracle inequalities 

Mathematics Subject Classification




We thank the editor and the reviewers for their insightful comments.


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Copyright information

© Sociedad de Estadística e Investigación Operativa 2018

Authors and Affiliations

  1. 1.Department of Data Sciences and OperationsUniversity of Southern California Los AngelesUSA
  2. 2.Department of StatisticsTexas A&M UniversityCollege StationUSA
  3. 3.Departments of Statistics and BiostatisticsUniversity of WashingtonSeattleUSA
  4. 4.Flatiron InstituteSimons FoundationNew YorkUSA

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