Add to ORKGLinear regression analysis has become a fundamental tool in experimental sciences. We propose a new method for parameter estimation in linear models. The 'Generalized Ordered Linear Regression with Regularization' (GOLRR) uses various loss functions (including the o-insensitive ones), ordered weighted averaging of the residuals, and regularization. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend not only on the values but also on the order of the model residuals obtained for the current iteration. Such regression problem may be transformed into the iterative reweighted least squares scenario. The conjugate gradient algorithm is used to minimize the...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
SUMMARY. This paper proposes a modification of the Fisher–Scoring method, an algorithm which is wide...
We intrduce a new algorithm for 1L regularized generalized linear models. The 1L regularization proc...
We introduce a path following algorithm for "L" 1-regularized generalized linear models. The "L" 1-r...
AbstractA computationally efficient method to estimate seemingly unrelated regression equations mode...
In this paper, we propose an approach for learning regression models efficiently in an environment w...
A computationally efficient method to estimate seemingly unrelated regression equations models with ...
Following a discussion on the general form of regularization for semi-supervised learning, we propos...
none2A computationally efficient method to estimate seemingly unrelated regression equations models ...
An algorithm for computing the exact least trimmed squares (LTS) estimator of the standard regressio...
In this thesis we develop a method for efficient model building in nonlinear members of the GLM fami...
AbstractGeneralized linear models are widely used in statistical techniques. As an extension, genera...
Not AvailableIn regression modeling, often a restriction that regression coefficients are non-negati...
In regression modeling, often a restriction that regression coefficients are non-negative is faced. ...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
SUMMARY. This paper proposes a modification of the Fisher–Scoring method, an algorithm which is wide...
We intrduce a new algorithm for 1L regularized generalized linear models. The 1L regularization proc...
We introduce a path following algorithm for "L" 1-regularized generalized linear models. The "L" 1-r...
AbstractA computationally efficient method to estimate seemingly unrelated regression equations mode...
In this paper, we propose an approach for learning regression models efficiently in an environment w...
A computationally efficient method to estimate seemingly unrelated regression equations models with ...
Following a discussion on the general form of regularization for semi-supervised learning, we propos...
none2A computationally efficient method to estimate seemingly unrelated regression equations models ...
An algorithm for computing the exact least trimmed squares (LTS) estimator of the standard regressio...
In this thesis we develop a method for efficient model building in nonlinear members of the GLM fami...
AbstractGeneralized linear models are widely used in statistical techniques. As an extension, genera...
Not AvailableIn regression modeling, often a restriction that regression coefficients are non-negati...
In regression modeling, often a restriction that regression coefficients are non-negative is faced. ...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
The paper proposes a novel construction algorithm for generalized Gaussian kernel regression models....
SUMMARY. This paper proposes a modification of the Fisher–Scoring method, an algorithm which is wide...