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Durham University

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Publication details for Mr Tathagata Basu

Basu, Tathagata, Einbeck, Jochen & Troffaes, Matthias (2020), A sensitivity analysis and error bounds for the adaptive lasso, in Irigoien, I., Lee, D.-J., Martinez-Minaya, J. & Rodriguez-Alvarez, M.X. eds, International Workshop on Statistical Modelling. Bilbao, Universidad del Pais Vasco, 278-281.

Author(s) from Durham


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.