Add to ORKGWe consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross-country growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts
Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) ...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
In this work we discuss a novel model prior probability for variable selection in linear regression....
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
We examine the issue of variable selection in linear regression modeling, where we have a potentiall...
We examine the issue of variable selection in linear regression have a potentially large amount of ...
AbstractWe examine the issue of variable selection in linear regression modelling, where we have a p...
This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian mode...
In contrast to a posterior analysis given a particular sampling model, posterior model probabilities...
Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coher...
We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Mod...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Thesis (Ph.D.)--University of Washington, 2023Choosing a statistical model and accounting for uncert...
We consider the specification of prior distributions for Bayesian model comparison, focusing on regr...
Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) ...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
In this work we discuss a novel model prior probability for variable selection in linear regression....
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
We examine the issue of variable selection in linear regression modeling, where we have a potentiall...
We examine the issue of variable selection in linear regression have a potentially large amount of ...
AbstractWe examine the issue of variable selection in linear regression modelling, where we have a p...
This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian mode...
In contrast to a posterior analysis given a particular sampling model, posterior model probabilities...
Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coher...
We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Mod...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Thesis (Ph.D.)--University of Washington, 2023Choosing a statistical model and accounting for uncert...
We consider the specification of prior distributions for Bayesian model comparison, focusing on regr...
Model uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) ...
The method of model averaging has become an important tool to deal with model uncertainty, in parti...
In this work we discuss a novel model prior probability for variable selection in linear regression....