Oracle inequalities for weighted group lasso in high-dimensional misspecified Cox models

Autor:	Yijun Xiao, Ting Yan, Huiming Zhang, Yuanyuan Zhang
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Proportional hazard model Partial likelihood Time-dependent data Weighted group Lasso Oracle inequalities Suprema of empirical processes Mathematics QA1-939
Zdroj:	Journal of Inequalities and Applications, Vol 2020, Iss 1, Pp 1-33 (2020)
Druh dokumentu:	article
ISSN:	1029-242X
DOI:	10.1186/s13660-020-02517-3
Popis:	Abstract We study the nonasymptotic properties of a general norm penalized estimator, which include Lasso, weighted Lasso, and group Lasso as special cases, for sparse high-dimensional misspecified Cox models with time-dependent covariates. Under suitable conditions on the true regression coefficients and random covariates, we provide oracle inequalities for prediction and estimation error based on the group sparsity of the true coefficient vector. The nonasymptotic oracle inequalities show that the penalized estimator has good sparse approximation of the true model and enables to select a few meaningful structure variables among the set of features.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/761310f14adc404ca1be1c1c8c92263c Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.