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A sensitivity analysis and error bounds for the adaptive lasso.

Basu, Tathagata and Einbeck, Jochen and Troffaes, Matthias (2020) 'A sensitivity analysis and error bounds for the adaptive lasso.', in Proceedings of the 35th International Workshop on Statistical Modelling. , pp. 278-281.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Date accepted:02 June 2020
Date deposited:06 October 2020
Date of first online publication:20 July 2020
Date first made open access:08 October 2020

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