Shrinkage and penalized estimation in semi-parametric models with multicollinear data

DOI10.1080/00949655.2016.1171868OpenAlexW2339965784MaRDI QIDQ5221551

Bahadır Yüzbaşı, S. Ejaz Ahmed

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2016.1171868

zbMATH Keywords

kernel smoothingshrinkage estimationridge regressionpretest estimationpenalty estimationasymptotic and simulation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05)

Related Items (5)

Liu-type shrinkage estimations in linear models ⋮ Double shrunken selection operator ⋮ Improved mixed model for longitudinal data analysis using shrinkage method ⋮ Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime ⋮ Ridge-type pretest and shrinkage estimations in partially linear models

Uses Software

• R

Cites Work

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• Absolute penalty and shrinkage estimation in partially linear models
• Estimation in partially linear models and numerical comparisons
• Asymptotics for Lasso-type estimators.
• The laws of the iterated logarithm of some estimates in partly linear models
• Penalty, shrinkage and pretest strategies. Variable selection and estimation
• Ridge regression methodology in partial linear models with correlated errors
• SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELS
• Adaptive parametric test in a semiparametric regression model
• Asymptotic theory for partly linear models
• Ridge Regression: Applications to Nonorthogonal Problems

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