Least trimmed squares ridge estimation in partially linear regression models

DOI10.1080/00949655.2015.1128433OpenAlexW2375215062MaRDI QIDQ5222515

Mahdi Roozbeh, Morteza Amini

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/10.1080/00949655.2015.1128433

zbMATH Keywords

kernel smoothinggeneralized cross validationleast trimmed squares estimatorsemiparametric regression modelleverage pointsoutlierdetection

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Linear regression; mixed models (62J05) Diagnostics, and linear inference and regression (62J20)

Related Items (9)

Using Improved Robust Estimators to Semiparametric Model with High Dimensional Data ⋮ Identification for partially linear regression model with autoregressive errors ⋮ Robust restricted Liu estimator in censored semiparametric linear models ⋮ Robust partial residuals estimation in semiparametric partially linear model ⋮ Ridge estimation in semi-parametric regression models under the stochastic restriction and correlated elliptically contoured errors ⋮ Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models ⋮ Outlier-free merging of homogeneous groups of pre-classified observations under contamination ⋮ Uncertain stochastic ridge estimation in partially linear regression models with elliptically distributed errors ⋮ Covariance matrices of S robust regression estimators

Uses Software

• robustbase

Cites Work

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