Add to ORKGrobreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM estimator, the Huber and bisquare M estimator, the S estimator, as well as quantile regression, each supporting robust standard errors based on influence functions. Furthermore, basic implementations of LMS/LQS (least median/quantile of squares) and LTS (least trimmed squares) are provided. An older version of this routine is available as -robreg10-
A regression estimator is said to be robust if it is still reliable in the presence of outliers. On ...
In this article, we describe Robinson's (1988, Econometrica 56: 931-954) double residual semiparamet...
The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the param...
robreg provides a number of robust estimators for linear regression models. Among them are the high ...
ROBREG.SRC estimates a linear regression robustly and rapidly. ROBREG.SRC should be especially usefu...
In regression analysis, the presence of outliers in the dataset can strongly distort the classical l...
In regression analysis, the presence of outliers in the data set can strongly distort the classical ...
Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oste...
-xtrobreg- provides robust pairwise-differences estimators and robust first-differences estimators f...
Robust estimators of the seemingly unrelated regression model are considered. First, S-estimators ar...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Robusne statističke metode osmišljene su kako bi se suzbili neki od problema koji se pojavljuju kod ...
Proc RobustReg is an experimental procedure in SAS/STAT ® version 9. It implements the most commonly...
Ordinary least-squares (OLS) estimates for a linear model are very sensitive to unusual values in th...
En esta Tesis presentamos una nueva clase de estimadores (que llamaremos REWLS) para el modelo de Re...
A regression estimator is said to be robust if it is still reliable in the presence of outliers. On ...
In this article, we describe Robinson's (1988, Econometrica 56: 931-954) double residual semiparamet...
The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the param...
robreg provides a number of robust estimators for linear regression models. Among them are the high ...
ROBREG.SRC estimates a linear regression robustly and rapidly. ROBREG.SRC should be especially usefu...
In regression analysis, the presence of outliers in the dataset can strongly distort the classical l...
In regression analysis, the presence of outliers in the data set can strongly distort the classical ...
Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oste...
-xtrobreg- provides robust pairwise-differences estimators and robust first-differences estimators f...
Robust estimators of the seemingly unrelated regression model are considered. First, S-estimators ar...
Ordinary least-squares (OLS) estimators for a linear model are very sensitive to unusual values in t...
Robusne statističke metode osmišljene su kako bi se suzbili neki od problema koji se pojavljuju kod ...
Proc RobustReg is an experimental procedure in SAS/STAT ® version 9. It implements the most commonly...
Ordinary least-squares (OLS) estimates for a linear model are very sensitive to unusual values in th...
En esta Tesis presentamos una nueva clase de estimadores (que llamaremos REWLS) para el modelo de Re...
A regression estimator is said to be robust if it is still reliable in the presence of outliers. On ...
In this article, we describe Robinson's (1988, Econometrica 56: 931-954) double residual semiparamet...
The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the param...