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Datta, A. and Zou, H. (2017). Cocolasso for high-dimensional error-in-variables regression. The Annals of Statistics., 45, 2400-2426.Abhirup Datta and Hui Zou. Cocolasso for high-dimensional error-in- variables regression. The Annals of Statistics, 45(6):2400-2426, 2017....
eivreg — Errors-in-variables regression Description Remarks and examples Quick start Stored results Menu Methods and formulas Syntax References Options Also see Description eivreg fits errors-in-variables regression models when one or more of the independent variables are measured with error. To use ...
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Error varianceJackknife empirical likelihoodVarying-coefficient errors-in-variables modelalpha $$ α -Mixing62E2062J10For the partially linear varying-coefficient model when the parametric covariates are measured with additive errors, the estimator of the error variance is defined based on residuals of ...
The goal of hdme is to provide penalized regression methods for High-Dimensional Measurement Error problems (errors-in-variables). Installation Installhdmefrom CRAN using. install.packages("hdme") You can install the latest development version from github with: ...
This paper mainly introduces the method of empirical likelihood and its applications on two different models. We discuss theempirical likelihood inference on fixed-effect parameter in mixed-effects model with error-in-variables. We first considera linear mixed-effects model with measurement errors in ...
Without measurement errors in predictors, discontinuity of a nonparametric regression function at unknown locations could be estimated using a number of existing approaches. However, it becomes a challenging problem when the predictors contain measurement errors. In this paper, an error-in-variables jump...
In this paper, the estimation accuracy of the two methods is analyzed statistically for both errors-in-variables (EIV) and output error models (OEM). Numerical comparisons between these two estimates are also done under different signal-to-noise ratios (SNRs). The results suggest that TML and...
underprediction of the action of the Burgers operator in the presence of noise in the independent variable. We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy ...