Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mec...

Standard statistical practice ignores model uncertainty Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data This approach ignores the uncertainty in model selection leading to over con dent inferences and decisions that are more risky than one thinks they are Bayesian model averaging BMA provides a coherent mechanism ...

Empirical growth research faces a high degree of model uncertainty. The current paper deals with the fundamental issue of parameter estimation under model uncertainty, and compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-av...

This paper studies combining `1 regularization and Markov chain Monte Carlo model composition techniques for Bayesian model averaging (BMA). The main idea is to resolve the model uncertainty issues arising from path point selection by treating the `1 regularization path as a model space for BMA. The method is developed for linear and logistic regression, and applied to sample classification in ...

This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averag...

The topic of my dissertation research is Bayesian methods in the social sciences. There will be several parts to the dissertation. One part, the focus of this proposal, is a novel method called tuned Bayesian model averaging (tBMA) that modifies Bayesian model averaging (BMA) by using the data to inform the prior specification. BMA is a tool that accounts for model uncertainty in typical regres...

Bayesian Model Averaging (BMA) is well known for improving predictive accuracy by averaging inferences over all models in the model space. However, Markov chain Monte Carlo (MCMC) sampling, as the standard implementation for BMA, encounters difficulties in even relatively simple model spaces. We introduce a minimum message length (MML) coupled MCMC methodology, which not only addresses these di...

BACKGROUND Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single "best" model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as ...

This study revisits the widely researched area of consumption function using Bayesian Model Averaging (BMA) for a panel EU countries to deal with uncertainty potential determinants, convergence club analysis construct homogeneous groups by income. BMA suggests that income is only variable found be strong determinant across different country groups, whereas other variables have varying importanc...