Add to ORKGWe define a new Bayesian predictor called the posterior weighted median (PWM) and compare its performance to several other predictors including the Bayes model average under squared error loss, the Barbieri-Berger median model predictor, the stacking predictor, and the model average predictor based on Akaike\u27s information criterion. We argue that PWM generally gives better performance than other predictors over a range of M-complete problems. This range is between the M-closed-M-complete boundary and the M-complete- M-open boundary. Indeed, as a problem gets closer to M-open, it seems that M-complete predictive methods begin to break down. Our comparisons rest on extensive simulations and real data examples
We define an extension of the posterior predictive $p$-value for multiple test statistics and establ...
There is now a large literature on optimal predictive model selection. Bayesian methodology based on...
Missing data are a common problem for both the construction and implementation of a prediction algor...
We define a new Bayesian predictor called the posterior weighted median (PWM) and compare its perfor...
In M-open problems where no true model can be conceptualized, it is common to back off from modeling...
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process i...
We discuss a robust solution to the problem of prediction. Extending Barndorff-Nielsen and Cox [1996...
Summary: We explore the use of a posterior predictive loss criterion for model selection for incompl...
<p>Collections of large volumes of rich and complex data has become ubiquitous in recent years, posi...
Model choice is a fundamental and much discussed activity in the analysis of data sets. Hierarchical...
Often the goal of model selection is to choose a model for future prediction, and it is natural to m...
This chapter presents a model averaging approach in the M-open setting using sample re-use methods t...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
The paper develops a computational method to deal with some open issues related to Bayesian model av...
Suppose we observe X ~ Nm(Aβ, σ2I) and would like to estimate the predictive density p(y|β) of a fut...
We define an extension of the posterior predictive $p$-value for multiple test statistics and establ...
There is now a large literature on optimal predictive model selection. Bayesian methodology based on...
Missing data are a common problem for both the construction and implementation of a prediction algor...
We define a new Bayesian predictor called the posterior weighted median (PWM) and compare its perfor...
In M-open problems where no true model can be conceptualized, it is common to back off from modeling...
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process i...
We discuss a robust solution to the problem of prediction. Extending Barndorff-Nielsen and Cox [1996...
Summary: We explore the use of a posterior predictive loss criterion for model selection for incompl...
<p>Collections of large volumes of rich and complex data has become ubiquitous in recent years, posi...
Model choice is a fundamental and much discussed activity in the analysis of data sets. Hierarchical...
Often the goal of model selection is to choose a model for future prediction, and it is natural to m...
This chapter presents a model averaging approach in the M-open setting using sample re-use methods t...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
The paper develops a computational method to deal with some open issues related to Bayesian model av...
Suppose we observe X ~ Nm(Aβ, σ2I) and would like to estimate the predictive density p(y|β) of a fut...
We define an extension of the posterior predictive $p$-value for multiple test statistics and establ...
There is now a large literature on optimal predictive model selection. Bayesian methodology based on...
Missing data are a common problem for both the construction and implementation of a prediction algor...