Tuning parameter selection for the adaptive LASSO in the autoregressive model

DOI10.1016/j.jkss.2016.10.005zbMath1485.62123OpenAlexW2554472417MaRDI QIDQ526980

Okyoung Na, Sangin Lee, Sunghoon Kwon

Publication date: 15 May 2017

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jkss.2016.10.005

zbMATH Keywords

subset selectionBayesian information criterionautoregressive modelleast absolute shrinkage and selection operatorpenalized estimation

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

Bayesian empirical likelihood inference and order shrinkage for autoregressive models ⋮ Penalized multiply robust estimation in high-order autoregressive processes with missing explanatory variables

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Estimation in high-dimensional linear models with deterministic design matrices
• Autoregressive process modeling via the Lasso procedure
• Order selection in nonstationary autoregressive models
• Some sharp performance bounds for least squares regression with \(L_1\) regularization
• One-step sparse estimates in nonconcave penalized likelihood models
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• The estimation of the order of an ARMA process
• Estimating the dimension of a model
• Bootstraps for time series
• Nonconcave penalized likelihood with a diverging number of parameters.
• On Bernstein-type inequalities for martingales.
• Calibrating nonconvex penalized regression in ultra-high dimension
• Pathwise coordinate optimization
• Fitting autoregressive models for prediction
• Large sample properties of the smoothly clipped absolute deviation penalized maximum likelihood estimation on high dimensions
• Global optimality of nonconvex penalized estimators
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• PREDICTION‐FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELS
• Selection of the order of an autoregressive model by Akaike's information criterion
• A cross-validatory method for dependent data
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• On a method of identification of best subset model from full ar-model
• Regression coefficient and autoregressive order shrinkage and selection via the lasso
• Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
• Estimation of stationary autoregressive models with the Bayesian LASSO
• Smoothly Clipped Absolute Deviation on High Dimensions
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• A general theory of concave regularization for high-dimensional sparse estimation problems

This page was built for publication: Tuning parameter selection for the adaptive LASSO in the autoregressive model