Estimation of error variance in one-way random model

Metrika ◽  
1996 ◽  
Vol 44 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Paul Chiou ◽  
Chien-Pai Han
Keyword(s):  
Error Variance ◽  
Random Model ◽  
Estimation Of Error

scholarly journals On Robust Estimation of Error Variance in (Highly) Robust Regression

2020 ◽  
Vol 20 (1) ◽  
pp. 6-14 ◽  
Author(s):  
Jan Kalina ◽  
Jan Tichavský
Keyword(s):  
Robust Estimation ◽  
Robust Regression ◽  
Error Variance ◽  
Random Regression ◽  
Estimation Of Parameters ◽  
Least Trimmed Squares ◽  
Estimation Of Error ◽  
Accuracy And Precision ◽  
The Mean ◽  
Theoretical Results

AbstractThe linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes.

Estimation of error variance via ridge regression

Biometrika ◽  
2020 ◽  
Author(s):  
X Liu ◽  
S Zheng ◽  
X Feng
Keyword(s):  
Random Matrix Theory ◽  
Random Matrix ◽  
Ridge Regression ◽  
Matrix Theory ◽  
Numerical Study ◽  
Asymptotic Properties ◽  
Error Variance ◽  
High Dimensional ◽  
Finite Sample ◽  
Estimation Of Error

Summary We propose a novel estimator of error variance and establish its asymptotic properties based on ridge regression and random matrix theory. The proposed estimator is valid under both low- and high-dimensional models, and performs well not only in nonsparse cases, but also in sparse ones. The finite-sample performance of the proposed method is assessed through an intensive numerical study, which indicates that the method is promising compared with its competitors in many interesting scenarios.

Estimation of Error Variance from Smallest Ordered Contrasts

1963 ◽  
Vol 58 (301) ◽  
pp. 152-160 ◽  
Author(s):  
M. B. Wilk ◽  
R. Gnanadesikan ◽  
Anne E. Freeny
Keyword(s):  
Error Variance ◽  
Estimation Of Error

IMPROVED ESTIMATION OF ERROR VARIANCE IN GENERAL BALANCED MIXED MODELS

1992 ◽  
Vol 10 (3) ◽  
Author(s):  
Thomas Mathew ◽  
Bimal Kumar Sinha ◽  
Brajendra C. Sutradhar
Keyword(s):  
Mixed Models ◽  
Error Variance ◽  
Estimation Of Error
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