Results 1 
3 of
3
Bayesian model averaging
 STAT.SCI
, 1999
"... 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 overcon dent inferences and decisions tha ..."
Abstract

Cited by 43 (0 self)
 Add to MetaCart
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 overcon dent inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA haverecently emerged. We discuss these methods and present anumber of examples. In these examples, BMA provides improved outofsample predictive performance. We also provide a catalogue of
Bayesian Model Averaging in proportional hazard models: Assessing the risk of a stroke
 Applied Statistics
, 1997
"... Evaluating the risk of stroke is important in reducing the incidence of this devastating disease. Here, we apply Bayesian model averaging to variable selection in Cox proportional hazard models in the context of the Cardiovascular Health Study, a comprehensive investigation into the risk factors for ..."
Abstract

Cited by 29 (5 self)
 Add to MetaCart
Evaluating the risk of stroke is important in reducing the incidence of this devastating disease. Here, we apply Bayesian model averaging to variable selection in Cox proportional hazard models in the context of the Cardiovascular Health Study, a comprehensive investigation into the risk factors for stroke. We introduce a technique based on the leaps and bounds algorithm which e ciently locates and ts the best models in the very large model space and thereby extends all subsets regression to Cox models. For each independent variable considered, the method provides the posterior probability that it belongs in the model. This is more directly interpretable than the corresponding Pvalues, and also more valid in that it takes account of model uncertainty. Pvalues from models preferred by stepwise methods tend to overstate the evidence for the predictive value of a variable. In our data Bayesian model averaging predictively outperforms standard model selection methods for assessing
Bayesian Simultaneous Variable and Transformation Selection in Linear Regression
, 1999
"... We suggest a method for simultaneous variable and transformation selection based on posterior probabilities. A simultaneous approach avoids the problem that the order in which variable and transformation selection are performed might influence the choice of variables and transformations. The simulta ..."
Abstract
 Add to MetaCart
We suggest a method for simultaneous variable and transformation selection based on posterior probabilities. A simultaneous approach avoids the problem that the order in which variable and transformation selection are performed might influence the choice of variables and transformations. The simultaneous approach also allows for consideration of all possible models. We use a changepoint model, or "changepoint transformation," which can yield more interpretable models and transformations than the standard BoxTidwell approach. We also address the problem of model uncertainty in the selection of models. By averaging over models, we account for the uncertainty inherent in inference based on a single model chosen from the set of all possible models. We use a Markov chain Monte Carlo model composition (MC 3 ) method which allows us to average over linear regression models when the space of all possible models is very large. This considers the selection of variables and transformations a...