For the normal linear model variable selection problem, we propose selection criteria based on a fully Bayes formulation with a generalization of Zellner’s g-prior which allows for p \u3e n. A special case of the prior formulation is seen to yield tractable closed forms for marginal densities and Bayes factors which reveal new model evaluation characteristics of potential interest
We propose an automatic Bayesian approach to the selection of covariates and penalised splines trans...
I congratulate the authors of this very interesting paper on their work in which they implement my s...
In this paper, we consider theoretical and computational connections between six popular methods for...
For the normal linear model variable selection problem, we propose selection criteria based on a ful...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
textThere are numerous frequentist statistics variable selection methods such as Stepwise regression...
<p>The adoption of Zellner's g prior is a popular prior choice in Bayesian Model Averaging, although...
We examine the issue of variable selection in linear regression modeling, where we have a potentiall...
For the problem of variable selection for the normal linear model, fixed penalty selection criteria ...
textI consider the problem of variable selection for Generalized Linear Models (GLM). A great deal o...
Doctor of PhilosophyDepartment of StatisticsGyuhyeong GohBayesian model selection has enjoyed consid...
A hierarchical Bayesian formulation in Generalized Linear Models (GLMs) is proposed in this disserta...
The selection of variables in regression problems has occupied the minds of many statisticians. Seve...
Bayesian model selection with improper priors is not well-defined becauseof the dependence of the ma...
Abstract: This paper studies Bayesian variable selection in linear models with general spherically s...
We propose an automatic Bayesian approach to the selection of covariates and penalised splines trans...
I congratulate the authors of this very interesting paper on their work in which they implement my s...
In this paper, we consider theoretical and computational connections between six popular methods for...
For the normal linear model variable selection problem, we propose selection criteria based on a ful...
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objec...
textThere are numerous frequentist statistics variable selection methods such as Stepwise regression...
<p>The adoption of Zellner's g prior is a popular prior choice in Bayesian Model Averaging, although...
We examine the issue of variable selection in linear regression modeling, where we have a potentiall...
For the problem of variable selection for the normal linear model, fixed penalty selection criteria ...
textI consider the problem of variable selection for Generalized Linear Models (GLM). A great deal o...
Doctor of PhilosophyDepartment of StatisticsGyuhyeong GohBayesian model selection has enjoyed consid...
A hierarchical Bayesian formulation in Generalized Linear Models (GLMs) is proposed in this disserta...
The selection of variables in regression problems has occupied the minds of many statisticians. Seve...
Bayesian model selection with improper priors is not well-defined becauseof the dependence of the ma...
Abstract: This paper studies Bayesian variable selection in linear models with general spherically s...
We propose an automatic Bayesian approach to the selection of covariates and penalised splines trans...
I congratulate the authors of this very interesting paper on their work in which they implement my s...
In this paper, we consider theoretical and computational connections between six popular methods for...
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