Ordinary least-squares (OLS) estimates for a linear model are very sensitive to unusual values in the design space or outliers among unpredicted values. Even one single value may have a large effect on the parameter estimates. This paper aims to focus, review and describe some available and popular, robustregressiontechniques, including compare them in terms of efficiency. Recent developed robust regression techniques also discussed
The linear regression model requires robust estimation of parameters, if the measured data are conta...
The present study investigates parameter estimation under the simple linear regression model for sit...
Four approaches to linear robust regression analysis are presented. In the presence of outliers or b...
Ordinary least-squares (OLS) estimates for a linear model are very sensitive to unusual values in th...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Regression analysis is one of the most extensively used statistical tools applied across different f...
The theory of robustness developed by Huber and Hampel laid the foundation for finding practical sol...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
A Monte Carlo simulation was used to generate data for a comparison of five robust regression estima...
This study examines robust regression methods which are used for the solution of problems caused by ...
Master of ScienceDepartment of StatisticsWeixin YaoIn practice, when applying a statistical method i...
Four approaches to linear robust regression analysis are presented. In the presence of outliers or b...
Design of Experiments (DOE) is a very powerful statistical methodology, especially when used with li...
The linear regression model requires robust estimation of parameters, if the measured data are conta...
The present study investigates parameter estimation under the simple linear regression model for sit...
Four approaches to linear robust regression analysis are presented. In the presence of outliers or b...
Ordinary least-squares (OLS) estimates for a linear model are very sensitive to unusual values in th...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Regression analysis is one of the most extensively used statistical tools applied across different f...
The theory of robustness developed by Huber and Hampel laid the foundation for finding practical sol...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
A Monte Carlo simulation was used to generate data for a comparison of five robust regression estima...
This study examines robust regression methods which are used for the solution of problems caused by ...
Master of ScienceDepartment of StatisticsWeixin YaoIn practice, when applying a statistical method i...
Four approaches to linear robust regression analysis are presented. In the presence of outliers or b...
Design of Experiments (DOE) is a very powerful statistical methodology, especially when used with li...
The linear regression model requires robust estimation of parameters, if the measured data are conta...
The present study investigates parameter estimation under the simple linear regression model for sit...
Four approaches to linear robust regression analysis are presented. In the presence of outliers or b...
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