Add to ORKGThis paper is devoted to model selection in logistic regression. We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. This selection is done by using penalized maximum likelihood criteria. We propose in this context a completely data-driven criteria based on the slope heuristics. We prove non asymptotic oracle inequalities for selected estimators. Theoretical results are illustrated through simulation studies
Statistical model selection is a great challenge when the number of accessible measurements is much ...
In this paper we consider model selection problem using samples of small or moderate size where each...
... In this article, penalized likelihood approaches are proposed to handle these kinds of problems....
International audienceWe propose a model selection procedure in the context of matched case-control ...
This paper considers the construction of model selection procedures based on choosing the model with...
A linearised approximation of the log-likelihood objective function is presented as a potential alte...
PhD (Science with Business Mathematics), North-West University, Potchefstroom CampusLogistic regress...
International audienceWe build penalized least-squares estimators using the slope heuristic and resa...
A class of variable selection procedures for parametric models via nonconcave penalized likelihood i...
Many popular methods of model selection involve minimizing a penalized function of the data (such as...
This paper is concerned with model selection based on penalized maximized log likelihood functions. ...
Information criteria (IC) are used widely to choose between competing alternative models. When these...
Since the proposal of the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996)...
Model selection methods are commonly used to identify the best approximation that explains the data....
<p>Model selection values of the candidate models of binary logistic regression.</p
Statistical model selection is a great challenge when the number of accessible measurements is much ...
In this paper we consider model selection problem using samples of small or moderate size where each...
... In this article, penalized likelihood approaches are proposed to handle these kinds of problems....
International audienceWe propose a model selection procedure in the context of matched case-control ...
This paper considers the construction of model selection procedures based on choosing the model with...
A linearised approximation of the log-likelihood objective function is presented as a potential alte...
PhD (Science with Business Mathematics), North-West University, Potchefstroom CampusLogistic regress...
International audienceWe build penalized least-squares estimators using the slope heuristic and resa...
A class of variable selection procedures for parametric models via nonconcave penalized likelihood i...
Many popular methods of model selection involve minimizing a penalized function of the data (such as...
This paper is concerned with model selection based on penalized maximized log likelihood functions. ...
Information criteria (IC) are used widely to choose between competing alternative models. When these...
Since the proposal of the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996)...
Model selection methods are commonly used to identify the best approximation that explains the data....
<p>Model selection values of the candidate models of binary logistic regression.</p
Statistical model selection is a great challenge when the number of accessible measurements is much ...
In this paper we consider model selection problem using samples of small or moderate size where each...
... In this article, penalized likelihood approaches are proposed to handle these kinds of problems....
