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Zhang, C.H. and Huang, J. (2008) The Sparsity and Bias of the Lasso Selection in High-Dimensional Linear Regression. Annals of Statistics, 36, 1567-1594.
https://doi.org/10.1214/07-AOS520

has been cited by the following article:

• TITLE: Variance Estimation for High-Dimensional Varying Index Coefficient Models

  AUTHORS: Miao Wang, Hao Lv, Yicun Wang

  KEYWORDS: High-Dimensional Data, Refitted Cross-Validation, Varying Index Coefficient Models, Variance Estimation

  JOURNAL NAME: Open Journal of Statistics, Vol.9 No.5, October 8, 2019

  ABSTRACT: This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficients of the parameter part of the Varying Index Coefficient Model (VICM), while the unknown function part uses the B-spline to expand. Moreover, we combine the above two estimation methods under the assumption of high-dimensional data. The results of data simulation and empirical analysis show that for the varying index coefficient model, the re-adjusted cross-validation method is better in terms of accuracy and stability than traditional methods based on ordinary least squares.