Asymptotic theory of debiased regularized M-estimators KU Leuven
Regression coefficient estimation by regularized estimators such as the Lasso, introduces bias and its selection of coefficients makes classical inference methods inappropriate. Concerning debiasing the regularized estimator, recent literature focuses mostly on the case of the least squares loss, leaving other loss functions such as the quantile loss underexplored. My research proposal mainly investigates the general class of debiased ...