Add to ORKGThis paper proposes a new approach for model selection and applies it to a classical time series modeling problem. In contrast to conventional model selection methods like AIC and BIC, whose penalty terms typically depend only on the number of model parameters, the proposed model selection method also takes the values of the model parameters and the sets of candidate models into account. A brief sketch of a Bayesian further development of this method is given within the framework of the linear regression model
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
The principle that the simplest model capable of describing observed phenomena should also correspon...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...
In this study we are proposing a Bayesian model selection methodology, where the best model from the...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The qua...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
Many popular methods of model selection involve minimizing a penalized function of the data (such as...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
The problem of statistical model selection in econometrics and statistics is reviewed. Model selecti...
International audienceThis paper studies the problem of model selection in a large class of causal t...
In this chapter we survey Bayesian approaches for variable selection and model choice in regression ...
We discuss model selection, both from a Bayes and Classical point of view. Our presentation introduc...
Model selection is an important part of any statistical analysis, and indeed is central to the pursu...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
The principle that the simplest model capable of describing observed phenomena should also correspon...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...
In this study we are proposing a Bayesian model selection methodology, where the best model from the...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The qua...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
Many popular methods of model selection involve minimizing a penalized function of the data (such as...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
The problem of statistical model selection in econometrics and statistics is reviewed. Model selecti...
International audienceThis paper studies the problem of model selection in a large class of causal t...
In this chapter we survey Bayesian approaches for variable selection and model choice in regression ...
We discuss model selection, both from a Bayes and Classical point of view. Our presentation introduc...
Model selection is an important part of any statistical analysis, and indeed is central to the pursu...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
The principle that the simplest model capable of describing observed phenomena should also correspon...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...