2016/03/20 by Niharika Gauraha, Gauraha, Niharika
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #FOS: Mathematics #Risk and Portfolio Optimization #Statistical Methods and Inference #Statistics Theory (math.ST) #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.1603.06177
openalex publication_date 2016/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study various constraints and conditions on the true coefficient vector and on the design matrix to establish non-asymptotic oracle inequalities for the prediction error, estimation accuracy and variable selection for the Lasso estimator in high dimensional sparse regression models. We review results from the literature and we provide simpler and detailed derivation for several boundedness theorems. In addition, we complement the theory with illustrated examples.