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.

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.

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.

scholarly journals Model averaging methods for the evaluation of dose-response model uncertainty when assessing the suitability of studies for estimating risk

2020 ◽  
Vol 143 ◽  
pp. 105857
Author(s):  
William Mendez ◽  
Kan Shao ◽  
Janice S. Lee ◽  
Ila Cote ◽  
Ingrid L. Druwe ◽  
...  
Keyword(s):  
Dose Response ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Response Model ◽  
Averaging Methods

scholarly journals Characterizing the effects of sex, APOE ɛ4, and literacy on mid-life cognitive trajectories: Application of Information-Theoretic model-averaging and multi-model inference techniques to the Wisconsin Registry for Alzheimer’s Prevention Study

2017 ◽  
Author(s):  
Rebecca L. Koscik ◽  
Derek L. Norton ◽  
Samantha L. Allison ◽  
Erin M. Jonaitis ◽  
Lindsay R. Clark ◽  
...  
Keyword(s):  
Model Selection ◽  
Cognitive Decline ◽  
Model Averaging ◽  
Parameter Estimates ◽  
Theoretic Model ◽  
Traditional Model ◽  
Test Model ◽  
Prevention Study ◽  
Information Theoretic ◽  
Modifiable Factors

ObjectiveIn this paper we apply Information-Theoretic (IT) model averaging to characterize a set of complex interactions in a longitudinal study on cognitive decline. Prior research has identified numerous genetic (including sex), education, health and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find models that best fit the observed data; these techniques risk interpretations that only the selected predictors are important. In reality, several models may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates); inference from traditional model selection approaches can lead to overly confident conclusions.MethodHere we use longitudinal cognitive data from ~1550 late-middle aged adults the Wisconsin Registry for Alzheimer’s Prevention study to examine the effects of sex, Apolipoprotein E (APOE) ɛ4 allele (non-modifiable factors), and literacy achievement (modifiable) on cognitive decline. For each outcome, we applied IT model averaging to a model set with combinations of interactions among sex, APOE, literacy, and age.ResultsFor a list-learning test, model-averaged results showed better performance for women vs men, with faster decline among men; increased literacy was associated with better performance, particularly among men. APOE had less of an effect on cognitive performance in this age range (~40-70).ConclusionsThese results illustrate the utility of the IT approach and point to literacy as a potential modifier of decline. Whether the protective effect of literacy is due to educational attainment or intrinsic verbal intellectual ability is the topic of ongoing work.

scholarly journals Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

2021 ◽  
Author(s):  
Tomas Havranek ◽  
Roman Horvath ◽  
Ali Elminejad
Keyword(s):  
Labor Supply ◽  
Publication Bias ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Extensive Margin ◽  
Real Business Cycle ◽  
Intertemporal Substitution ◽  
The Mean ◽  
Frisch Elasticity ◽  
Frequentist Model Averaging

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models and models of taxation. We show that, for the extensive margin elasticity, two biases conspire to systematically produce large positive estimates when the elasticity is in fact zero. Among 723 estimates in 36 studies, the mean reported elasticity is 0.5. One half of that number is due to publication bias: larger estimates are reported preferentially. The other half is due to identification bias: studies with less exogenous time variation in wages report larger elasticities. Net of the biases, the literature implies a zero mean elasticity and, with 95% confidence, is inconsistent with calibrations above 0.25. To derive these results we collect 23 variables that reflect the context in which the elasticity was obtained, use nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to address model uncertainty.

Sign in / Sign up

Export Citation Format

Share Document