Add to ORKGRecently the concept of regression depth has been introduced [1]. The deepest regression (DR) is a method for linear regression which is defined as the fit with the best depth relative to the data. In this paper we explain the properties of the DR and give some applications of deepest regression in analytical chemistry which involve regression through the origin, polynomial regression, the Michaelis-Menten model, and censored responses.
Deep learning gained a lot of traction in the machine learning community. The performance of these m...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
AbstractThis paper is concentrated on the polynomial regression model, which is useful when there is...
Recently the concept of regression depth has been introduced [J. Am. Stat. Assoc. 94 (1999) 388]. Th...
Deepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and M. Huber...
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
Chemometrics is a chemical discipline in which mathematical and statistical techniques are applied t...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
The deepest regression method is such a method of estimation of regression parameters that the maxi...
AbstractGlobal depth, tangent depth and simplicial depths for classical and orthogonal regression ar...
A regression algorithm estimates the value of the target (response) as a function of the predictors ...
this paper. Procedures built by PolyAnalyst are treated as regression models which are nonlinear in ...
Deep learning gained a lot of traction in the machine learning community. The performance of these m...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
AbstractThis paper is concentrated on the polynomial regression model, which is useful when there is...
Recently the concept of regression depth has been introduced [J. Am. Stat. Assoc. 94 (1999) 388]. Th...
Deepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and M. Huber...
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
Chemometrics is a chemical discipline in which mathematical and statistical techniques are applied t...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
The deepest regression method is such a method of estimation of regression parameters that the maxi...
AbstractGlobal depth, tangent depth and simplicial depths for classical and orthogonal regression ar...
A regression algorithm estimates the value of the target (response) as a function of the predictors ...
this paper. Procedures built by PolyAnalyst are treated as regression models which are nonlinear in ...
Deep learning gained a lot of traction in the machine learning community. The performance of these m...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
AbstractThis paper is concentrated on the polynomial regression model, which is useful when there is...