Analytic and Bootstrap-after-Cross-Validation Methods for Selecting Penalty Parameters of High-Dimensional M-Estimators

Publikation: Working paperForskning


  • 2104

    Forlagets udgivne version, 990 KB, PDF-dokument

We develop two new methods for selecting the penalty parameter for the $\ell^1$-penalized high-dimensional M-estimator, which we refer to as the analytic and bootstrap-after-cross-validation methods. For both methods, we derive nonasymptotic error bounds for the corresponding $\ell^1$-penalized M-estimator and show that the bounds converge to zero under mild conditions, thus providing a theoretical justification for these methods. We demonstrate via simulations that the finite-sample performance of our methods is much better than that of previously available and theoretically justified methods.
Antal sider63
StatusUdgivet - 10 apr. 2021
NavnUniversity of Copenhagen. Institute of Economics. Discussion Papers (Online)


Antal downloads er baseret på statistik fra Google Scholar og

Ingen data tilgængelig

ID: 288855332