Sparsity considerations for dependent variables

DOI10.1214/11-EJS626zbMath1274.62462arXiv1102.1615OpenAlexW3100634981MaRDI QIDQ1952207

Paul Doukhan, Pierre Alquier

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1102.1615

zbMATH Keywords

penalizationdensity estimationweak dependenceregression estimationsparsityLASSOdeviation of empirical meanestimation in high dimension

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items

\(\ell_1\)-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors, Lasso guarantees for \(\beta \)-mixing heavy-tailed time series, Variable selection in multivariate linear models with high-dimensional covariance matrix estimation, Lasso with long memory regression errors, Matrix factorization for multivariate time series analysis, Bridge Estimation for Linear Regression Models with Mixing Properties, Penalized regression models with autoregressive error terms, Adaptive lasso for linear regression models with ARMA-GARCH errors, Deviation inequalities for separately Lipschitz functionals of composition of random functions

Uses Software

• PDCO

Cites Work

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