Improving the prediction performance of the Lasso by subtracting the additive structural noises

DOI10.1007/S00180-018-0849-0zbMath1417.62070OpenAlexW2899676570MaRDI QIDQ1729359

Mahdi Roozbeh, Morteza Amini

Publication date: 27 February 2019

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-018-0849-0

zbMATH Keywords

kernel smoothingsparsityLassohigh dimensionaladditive partially linear model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Generalized linear models (logistic models) (62J12)

Related Items (1)

Identification for partially linear regression model with autoregressive errors

Uses Software

• SemiPar
• Fahrmeir
• gamair

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Nearly unbiased variable selection under minimax concave penalty
• Estimation of semivarying coefficient time series models with ARMA errors
• Estimation and variable selection for generalized additive partial linear models
• Component selection and smoothing in multivariate nonparametric regression
• Variable selection in nonparametric additive models
• High-dimensional additive modeling
• Direct generalized additive modeling with penalized likelihood.
• That BLUP is a good thing: The estimation of random effects. With comments and a rejoinder by the author
• Nonconcave penalized likelihood with a diverging number of parameters.
• Least angle regression. (With discussion)
• Optimal partial ridge estimation in restricted semiparametric regression models
• Generalized Additive Models: Some Applications
• Mixed Effects Smoothing Spline Analysis of Variance
• Semiparametric Regression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparse Additive Models
• The consistency of model selection for dynamic Semi-varying coefficient models with autocorrelated errors
• Shrinkage Estimation of the Varying Coefficient Model
• Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
• Whittle Likelihood Estimation of Nonlinear Autoregressive Models With Moving Average Residuals
• Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
• Prediction Games and Arcing Algorithms
• Estimation and variable selection for semiparametric additive partial linear models
• Multivariate statistical modelling based on generalized linear models.

This page was built for publication: Improving the prediction performance of the Lasso by subtracting the additive structural noises