Estimation and hypothesis test for partial linear multiplicative models

DOI10.1016/j.csda.2018.06.017zbMath1469.62177OpenAlexW2876768420MaRDI QIDQ1796934

Zhenghui Feng, Jun Zhang, Heng Peng

Publication date: 17 October 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.06.017

zbMATH Keywords

kernel smoothingmodel checkingvariable selectionpartial linear modelslocal linear smoothing

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Nonparametric estimation (62G05)

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Additive distortion measurement errors regression models with exponential calibration, General least product relative error estimation for multiplicative regression models with or without multiplicative distortion measurement errors, Maximum nonparametric kernel likelihood estimation for multiplicative linear regression models, Single-index relative error regression models, A new relative error estimation for partially linear multiplicative model, Model checking for multiplicative linear regression models with mixed estimators, Incorporating relative error criterion to conformal prediction for positive data, Parallel integrative learning for large-scale multi-response regression with incomplete outcomes

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