Estimation of Error Variance in Regularized Regression Models via Adaptive Lasso

Estimation of error variance in a regression model is a fundamental problem in statistical modeling and inference. In high-dimensional linear models, variance estimation is a difficult problem, due to the issue of model selection. In this paper, we propose a novel approach for variance estimation th...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-06, Vol.10 (11), p.1937
Main Authors: Wang, Xin, Kong, Lingchen, Wang, Liqun
Format: Article
Language:English
Subjects:
Asymptotic properties
Error analysis (Mathematics)
Errors
Food science
high-dimensional linear model
Mathematical research
Maximum likelihood method
mean squared error bound
Monte Carlo simulation
natural adaptive lasso
Optimization
Parameter estimation
Parameters
Regression analysis
Regression models
regularized regression
Statistical analysis
Statistical inference
Statistical models
Variables
Variance
variance estimation
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Summary:Estimation of error variance in a regression model is a fundamental problem in statistical modeling and inference. In high-dimensional linear models, variance estimation is a difficult problem, due to the issue of model selection. In this paper, we propose a novel approach for variance estimation that combines the reparameterization technique and the adaptive lasso, which is called the natural adaptive lasso. This method can, simultaneously, select and estimate the regression and variance parameters. Moreover, we show that the natural adaptive lasso, for regression parameters, is equivalent to the adaptive lasso. We establish the asymptotic properties of the natural adaptive lasso, for regression parameters, and derive the mean squared error bound for the variance estimator. Our theoretical results show that under appropriate regularity conditions, the natural adaptive lasso for error variance is closer to the so-called oracle estimator than some other existing methods. Finally, Monte Carlo simulations are presented, to demonstrate the superiority of the proposed method.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10111937