On bandwidth selection in partial linear regression models under dependence

DOI10.1016/S0167-7152(02)00096-2zbMath0996.62033OpenAlexW2090489672MaRDI QIDQ1613093

Germán Aneiros-Pérez

Publication date: 5 September 2002

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(02)00096-2

zbMATH Keywords

kernel smoothingmixingbandwidth selectionpartial linear modelssemiparametric least-squares estimator

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05)

Related Items (4)

Plug-in bandwidth choice for estimation of nonparametric part in partial linear regression models with strong mixing errors ⋮ On inference for a semiparametric partially linear regression model with serially correlated errors ⋮ Testing in partial linear regression models with dependent errors ⋮ Local polynomial estimation in partial linear regression models under dependence

Cites Work

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• Central limit theorems under weak dependence
• Fixed design regression for time series: Asymptotic normality
• Mixing: Properties and examples
• Root-N-Consistent Semiparametric Regression
• Choice of bandwidth for kernel regression when residuals are correlated
• Moment bounds for non-stationary dependent sequences
• Second Order Approximation in the Partially Linear Regression Model
• Convergence of Distributions Generated by Stationary Stochastic Processes
• Plug-in bandwidth choice in partial linear models with autoregressive errors

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