Balanced estimation for high-dimensional measurement error models

DOI10.1016/j.csda.2018.04.009zbMath1469.62183OpenAlexW2799951999MaRDI QIDQ1663093

Yang Li, Chongxiu Yu, Zemin Zheng, Gao Rong Li

Publication date: 21 August 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.2018.04.009

zbMATH Keywords

model selectionhigh dimensionalitymeasurement errorsbalanced estimationcombined \(L_1\) and concave regularizationnearest positive semi-definite projection

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Sampling theory, sample surveys (62D05)

Related Items

Correlation coefficient-based measure for checking symmetry or asymmetry of a continuous variable with additive distortion, Screening Methods for Linear Errors-in-Variables Models in High Dimensions, L ₀ -regularization for high-dimensional regression with corrupted data, Scalable interpretable learning for multi-response error-in-variables regression, Logarithmic calibration for nonparametric multiplicative distortion measurement errors models

Uses Software

• ElemStatLearn

Cites Work

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