Add to ORKGTRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete. The first step involves trimming of the residuals and it makes the method robust against outliers. And the second step (in which only extreme outliers are ignored) restores the statistical efficiency for normal errors.\u3cbr/\u3e\u3cbr/\u3eDuring the last ten years the topic of high-breakdown methods is a very popular one among the developers of statistical methods. Some examples are Repeated Medians (Siegel, 1982), Least Median of Squares (Rousseeuw, 1984), S-estimators (Rousseeuw and Yohai, 1984), MM-estimators (Yohai, 1987) and τ-estimators (Yohai and Zamar, 1988). These very robust methods are extremely time-consuming and therefore only ...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
Regression analysis is the most commonly used statistical method in the world. Although few would ch...
This paper studies the smooth transition regression model where regressors are I(1) and errors are I...
TRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete....
TRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete....
TRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete....
The main objective of this study is to build a regression model by using multiple linear regression ...
A literature search shows that robust regression techniques are rarely used in applied econometrics....
A literature search shows that robust regression techniques are rarely used in applied econometrics....
Heteroskedastic regression data are modelled using a parameterized variance function. This procedure...
This study aims to evaluate the performance of multiple linear regression in estimating trade balanc...
Abstract: Robust methods are little applied (although much studied by statisticians). We monitor ver...
Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which he...
Heteroskedastic regression data are modelled using a parameterized variance function. This procedure...
Linear regression analysis is one of the most important statistical methods. Itexamines the linear r...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
Regression analysis is the most commonly used statistical method in the world. Although few would ch...
This paper studies the smooth transition regression model where regressors are I(1) and errors are I...
TRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete....
TRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete....
TRADE regression is a two-step reweighted least squares method. The name comes from TRim And DElete....
The main objective of this study is to build a regression model by using multiple linear regression ...
A literature search shows that robust regression techniques are rarely used in applied econometrics....
A literature search shows that robust regression techniques are rarely used in applied econometrics....
Heteroskedastic regression data are modelled using a parameterized variance function. This procedure...
This study aims to evaluate the performance of multiple linear regression in estimating trade balanc...
Abstract: Robust methods are little applied (although much studied by statisticians). We monitor ver...
Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which he...
Heteroskedastic regression data are modelled using a parameterized variance function. This procedure...
Linear regression analysis is one of the most important statistical methods. Itexamines the linear r...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
Regression analysis is the most commonly used statistical method in the world. Although few would ch...
This paper studies the smooth transition regression model where regressors are I(1) and errors are I...