Local linear smoothing for sparse high dimensional varying coefficient models

DOI10.1214/16-EJS1110zbMath1349.62313OpenAlexW2328212019MaRDI QIDQ276223

Enno Mammen, Eun Ryung Lee

Publication date: 3 May 2016

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1459967425

zbMATH Keywords

varying coefficient modelssemiparametric modelkernel methodoracle inequalityconsistent structural identificationlocal linear smoothinglocal stationary time seriespenalized methodssecond order cone programmingsparse estimation

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05) Nonparametric estimation (62G05)

Related Items

Variance estimation for sparse ultra-high dimensional varying coefficient models, On estimation in varying coefficient models for sparse and irregularly sampled functional data, Boosting high dimensional predictive regressions with time varying parameters, Penalized kernel quantile regression for varying coefficient models, Adaptively weighted group Lasso for semiparametric quantile regression models, Non-separable models with high-dimensional data, Nonparametric homogeneity pursuit in functional-coefficient models

Uses Software

• CVX
• glmnet

Cites Work

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