The Annals of Statistics
- Ann. Statist.
- Volume 47, Number 4 (2019), 2080-2116.
Perturbation bootstrap in adaptive Lasso
The Adaptive Lasso (Alasso) was proposed by Zou [J. Amer. Statist. Assoc. 101 (2006) 1418–1429] as a modification of the Lasso for the purpose of simultaneous variable selection and estimation of the parameters in a linear regression model. Zou [J. Amer. Statist. Assoc. 101 (2006) 1418–1429] established that the Alasso estimator is variable-selection consistent as well as asymptotically Normal in the indices corresponding to the nonzero regression coefficients in certain fixed-dimensional settings. In an influential paper, Minnier, Tian and Cai [J. Amer. Statist. Assoc. 106 (2011) 1371–1382] proposed a perturbation bootstrap method and established its distributional consistency for the Alasso estimator in the fixed-dimensional setting. In this paper, however, we show that this (naive) perturbation bootstrap fails to achieve second-order correctness in approximating the distribution of the Alasso estimator. We propose a modification to the perturbation bootstrap objective function and show that a suitably Studentized version of our modified perturbation bootstrap Alasso estimator achieves second-order correctness even when the dimension of the model is allowed to grow to infinity with the sample size. As a consequence, inferences based on the modified perturbation bootstrap will be more accurate than the inferences based on the oracle Normal approximation. We give simulation studies demonstrating good finite-sample properties of our modified perturbation bootstrap method as well as an illustration of our method on a real data set.
Ann. Statist., Volume 47, Number 4 (2019), 2080-2116.
Received: November 2016
Revised: February 2018
First available in Project Euclid: 21 May 2019
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Das, Debraj; Gregory, Karl; Lahiri, S. N. Perturbation bootstrap in adaptive Lasso. Ann. Statist. 47 (2019), no. 4, 2080--2116. doi:10.1214/18-AOS1741. https://projecteuclid.org/euclid.aos/1558425640
- Supplement to “Perturbation bootstrap in adaptive Lasso”. Details of the proofs and additional simulation studies are provided.