Statistical inferences for partially linear single-index models with error-prone linear covariates

DOI10.1016/j.jspi.2010.08.010zbMath1353.62044OpenAlexW2011981065MaRDI QIDQ607211

Zhen-Sheng Huang

Publication date: 19 November 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2010.08.010

zbMATH Keywords

partially linear single-index modelsgeneralized likelihood ratio testWilks phenomenonlocal linear methodancillary variables

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20)

Related Items (3)

Testing structural change in partially linear single-index models with error-prone linear covariates ⋮ Empirical-likelihood-based Test for Partially Linear Single-index Models with Error-prone Linear Covariates ⋮ Difference-based M-estimator of generalized semiparametric model with NSD errors

Cites Work

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• Penalized Spline Estimation for Partially Linear Single-Index Models
• Generalized likelihood ratio tests for the structure of semiparametric additive models
• Measurement Error in Nonlinear Models
• Nonparametric Inferences for Additive Models

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