A sensitivity analysis and error bounds for the adaptive lasso.

Tathagata Basu, Jochen Einbeck, Matthias Troffaes

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


Sparse regression is an efficient statistical modelling technique which is of major relevance for high dimensional problems. There are several ways of achieving sparse regression, the well-known lasso being one of them. However, lasso variable selection may not be consistent in selecting the true sparse model. Zou (2006) proposed an adaptive form of the lasso which overcomes this issue, and showed that data driven weights on the penalty term will result in a consistent variable selection procedure. Weights can be informed by a prior execution of least squares or ridge regression. Using a power parameter on the weights, we carry out a sensitivity analysis for this parameter, and derive novel error bounds for the Adaptive lasso.
Original languageEnglish
Title of host publicationProceedings of the 35th International Workshop on Statistical Modelling.
EditorsItziar Irigoien, Dae-Jin Lee, Joaquín Martínez-Minaya, María Xosé Rodríguez-Álvarez
Number of pages4
Publication statusPublished - 20 Jul 2020
Event35th International Workshop on Statistical Modelling 2020 - Bilbao, Spain
Duration: 20 Jul 202024 Jul 2020


Workshop35th International Workshop on Statistical Modelling 2020


  • sparse regression
  • statistical modelling technique
  • adaptive lasso
  • error bounds
  • sensitivity analysis


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