Tuning parameter estimation in penalized least squares methodology

E. Androulakis (1) , Christos Koukouvinos (1) , Kalliopi Mylona (1)

1 NTUA - National Technical University of Athens [Athens]

In general, tuning parameters are chosen by data-driven approaches, such as the commonly used generalized cross validation. In this article, we propose an alternative method for the derivation of the tuning parameter selector in penalized least squares framework, which can lead to an ameliorated estimate. Simulation studies are presented to support theoretical findings and a comparison of the Type I and Type II error rates, considering the $L_1$, the hard thresholding and the Smoothly Clipped Absolute Deviation penalty functions, is performed. The results are given in tables and discussion follows.

Mots clés: Physical Sciences

Domaines: Calcul [stat.CO]

E. Androulakis, Christos Koukouvinos, Kalliopi Mylona. Tuning parameter estimation in penalized least squares methodology. Communications in Statistics - Simulation and Computation, 2011, 40 (09), pp.1444-1457. ⟨10.1080/03610918.2011.575507⟩. ⟨hal-00703144⟩