Consistency results and confidence intervals for adaptive l1-penalized estimators of the high-dimensional sparse precision matrix

Autor: Avanesov, Valeriy, Polzehl, Jörg, Tabelow, Karsten
Rok vydání: 2016
Předmět:
DOI: 10.20347/wias.preprint.2229
Popis: In this paper we consider the adaptive '1-penalized estimators for the precision matrix in a finite-sample setting. We show consistency results and construct confidence intervals for the elements of the true precision matrix. Additionally, we analyze the bias of these confidence intervals. We apply the estimator to the estimation of functional connectivity networks in functional Magnetic Resonance data and elaborate the theoretical results in extensive simulation experiments.
Databáze: OpenAIRE