In this chapter we survey Bayesian approaches for variable selection and model choice in regression models. We explore the methodological developments and computational approaches for these methods. In conclusion we note the available software for their implementation.</p
In modern statistical and machine learning applications, there is an increasing need for developing ...
This paper proposes a new approach for model selection and applies it to a classical time series mod...
In this article, we study the connections between Bayesian methods and non-Bayesian methods for vari...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The qua...
Advisors: Sanjib Basu.Committee members: Michael Geline; Balakrishna Hosmane; Alan Polansky; Duchwan...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
Abstract. The selection of variables in regression problems has occupied the minds of many statistic...
In principle, the Bayesian approach to model selection is straightforward. Prior probability distrib...
We discuss model selection, both from a Bayes and Classical point of view. Our presentation introduc...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...
10.1016/j.csda.2010.01.036Computational Statistics and Data Analysis54123227-3241CSDA
In health sciences, identifying the leading causes that govern the behaviour of a response variable ...
In modern statistical and machine learning applications, there is an increasing need for developing ...
This paper proposes a new approach for model selection and applies it to a classical time series mod...
In this article, we study the connections between Bayesian methods and non-Bayesian methods for vari...
From a Bayesian viewpoint, the answer (in theory, at least) to the general model selection problem i...
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The qua...
Advisors: Sanjib Basu.Committee members: Michael Geline; Balakrishna Hosmane; Alan Polansky; Duchwan...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
Introduction A Bayesian approach to model selection proceeds as follows. Suppose that the data y ar...
Abstract. The selection of variables in regression problems has occupied the minds of many statistic...
In principle, the Bayesian approach to model selection is straightforward. Prior probability distrib...
We discuss model selection, both from a Bayes and Classical point of view. Our presentation introduc...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
Model selection is an important part of any statistical analysis, and indeed is cen-tral to the purs...
10.1016/j.csda.2010.01.036Computational Statistics and Data Analysis54123227-3241CSDA
In health sciences, identifying the leading causes that govern the behaviour of a response variable ...
In modern statistical and machine learning applications, there is an increasing need for developing ...
This paper proposes a new approach for model selection and applies it to a classical time series mod...
In this article, we study the connections between Bayesian methods and non-Bayesian methods for vari...
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