scholarly journals Bootstrap-after-Bootstrap Model Averaging for Reducing Model Uncertainty in Model Selection for Air Pollution Mortality Studies

2010 ◽  
Vol 118 (1) ◽  
pp. 131-136 ◽  
Author(s):  
Steven Roberts ◽  
Michael A. Martin
Keyword(s):  
Air Pollution ◽  
Model Selection ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Bootstrap Model ◽  
Selection For

scholarly journals Bayesian Model Averaging to Account for Model Uncertainty in Estimates of a Vaccine's Effectiveness

2021 ◽  
Author(s):  
Carlos R Oliveira ◽  
Eugene D Shapiro ◽  
Daniel M Weinberger
Keyword(s):  
Model Selection ◽  
Model Uncertainty ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Selection Methods ◽  
Final Model ◽  
Negative Case ◽  
Confounder Selection ◽  
Control Study

Vaccine effectiveness (VE) studies are often conducted after the introduction of new vaccines to ensure they provide protection in real-world settings. Although susceptible to confounding, the test-negative case-control study design is the most efficient method to assess VE post-licensure. Control of confounding is often needed during the analyses, which is most efficiently done through multivariable modeling. When a large number of potential confounders are being considered, it can be challenging to know which variables need to be included in the final model. This paper highlights the importance of considering model uncertainty by re-analyzing a Lyme VE study using several confounder selection methods. We propose an intuitive Bayesian Model Averaging (BMA) framework for this task and compare the performance of BMA to that of traditional single-best-model-selection methods. We demonstrate how BMA can be advantageous in situations when there is uncertainty about model selection by systematically considering alternative models and increasing transparency.

Model Uncertainty

2019 ◽  
pp. 175-206
Author(s):  
Jeffrey S. Racine
Keyword(s):  
Model Selection ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Quadratic Program ◽  
Selection Methods ◽  
Averaging Methods

This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.

Investigation on the Improvement of Prediction by Bootstrap Model Averaging

2006 ◽  
Vol 45 (01) ◽  
pp. 44-50 ◽  
Author(s):  
N. H. Augustin ◽  
W. Sauerbrei ◽  
N. Holländer
Keyword(s):  
Model Selection ◽  
Mean Squared Error ◽  
Model Averaging ◽  
Predictive Performance ◽  
Information Criterion ◽  
Full Model ◽  
Backward Elimination ◽  
Study Results ◽  
Model Selection Uncertainty ◽  
Bootstrap Model

Summary Objectives: We illustrate a recently proposed two-step bootstrap model averaging (bootstrap MA) approach to cope with model selection uncertainty. The predictive performance is investigated in an example and in a simulation study. Results are compared to those derived from other model selection methods. Methods: In the framework of the linear regression model we use the two-step bootstrap MA, which consists of a screening step to eliminate covariates thought to have no influence on the response, and a model-averaging step. We also apply the full model, variable selection using backward elimination based on Akaike’s Information Criterion (AIC), the Bayes Information Criterion (BIC) and the bagging approach. The predictive performance is measured by the mean squared error (MSE) and the coverage of confidence intervals for the true response. Results: We obtained similar results for all approaches in the example. In the simulation the MSE was reduced by all approaches in comparison to the full model. The smallest values are obtained for bootstrap MA. Only the bootstrap MA and the full model correctly estimated the nominal coverage. The backward elimination procedures led to substantial underestimation and bagging to an overestimation of the true coverage. The screening step of bootstrap MA eliminates most of the unimportant factors. Conclusion: The new bootstrap MA approach shows promising results for predictive performance. It increases practical usefulness by eliminating unimportant factors in the screening step.

The ellipsoidal nested sampling and the expression of the model uncertainty in measurements

2016 ◽  
Vol 30 (15) ◽  
pp. 1541002
Author(s):  
Gianpiero Gervino ◽  
Giovanni Mana ◽  
Carlo Palmisano
Keyword(s):  
Model Selection ◽  
Measurement Uncertainty ◽  
Model Uncertainty ◽  
Physical System ◽  
Bayesian Model ◽  
Model Averaging ◽  
Nested Sampling ◽  
Contour Lines ◽  
The Many ◽  
Numerical Strategy

In this paper, we consider the problems of identifying the most appropriate model for a given physical system and of assessing the model contribution to the measurement uncertainty. The above problems are studied in terms of Bayesian model selection and model averaging. As the evaluation of the “evidence” [Formula: see text], i.e., the integral of Likelihood × Prior over the space of the measurand and the parameters, becomes impracticable when this space has [Formula: see text] dimensions, it is necessary to consider an appropriate numerical strategy. Among the many algorithms for calculating [Formula: see text], we have investigated the ellipsoidal nested sampling, which is a technique based on three pillars: The study of the iso-likelihood contour lines of the integrand, a probabilistic estimate of the volume of the parameter space contained within the iso-likelihood contours and the random samplings from hyperellipsoids embedded in the integration variables. This paper lays out the essential ideas of this approach.

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