Variable selection in quantile regression when the models have autoregressive errors
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Publication:488595
DOI10.1016/j.jkss.2014.07.002zbMath1304.62097OpenAlexW2063581545MaRDI QIDQ488595
Publication date: 26 January 2015
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.2014.07.002
variable selectionSCAD penaltyES-algorithmpseudo dataautoregressive errorpenalized quantile regression
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Related Items (5)
Likelihood-based quantile autoregressive distributed lag models and its applications ⋮ Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors ⋮ Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation ⋮ Quantile regression for linear models with autoregressive errors using EM algorithm ⋮ Identification of Wiener model with internal noise using a cubic spline approximation-Bayesian composite quantile regression algorithm
Cites Work
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- A note on the monotonicity of the ES algorithm
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- Penalized regression models with autoregressive error terms
- Quantile Autoregression
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