Inference on the Primary Parameter of Interest with the Aid of Dimension Reduction Estimation

DOI10.1111/j.1467-9868.2010.00759.xzbMath1411.62061OpenAlexW2098495410WikidataQ57437906 ScholiaQ57437906MaRDI QIDQ4632652

Lexin Li, Li Xing Zhu, Li-ping Zhu

Publication date: 30 April 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9868.2010.00759.x

zbMATH Keywords

dimension reductionpartially linear single-index modelcentral partial mean subspacepartial ordinary least squares

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03) Nonparametric estimation (62G05) Point estimation (62F10)

Related Items (5)

A beyond multiple robust approach for missing response problem ⋮ Dimension reduction estimation for probability density with data missing at random when covariables are present ⋮ Partial projective resampling method for dimension reduction: with applications to partially linear models ⋮ Sufficient variable screening with high-dimensional controls ⋮ Empirical likelihood of quantile difference with missing response when high-dimensional covariates are present

This page was built for publication: Inference on the Primary Parameter of Interest with the Aid of Dimension Reduction Estimation