scholarly journals A comparison of design and model selection methods for supersaturated experiments

2010 ◽  
Vol 54 (12) ◽  
pp. 3158-3167 ◽  
Author(s):  
Christopher J. Marley ◽  
David C. Woods
Keyword(s):  
Model Selection ◽  
Selection Methods

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.

scholarly journals Learning Coefficient of Vandermonde Matrix-Type Singularities in Model Selection

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 561
Author(s):  
Miki Aoyagi
Keyword(s):  
Model Selection ◽  
Information Criteria ◽  
Learning Systems ◽  
Vandermonde Matrix ◽  
Selection Methods ◽  
Learning Models ◽  
Matrix Type ◽  
Log Canonical Threshold ◽  
Log Canonical ◽  
Blowing Up

In recent years, selecting appropriate learning models has become more important with the increased need to analyze learning systems, and many model selection methods have been developed. The learning coefficient in Bayesian estimation, which serves to measure the learning efficiency in singular learning models, has an important role in several information criteria. The learning coefficient in regular models is known as the dimension of the parameter space over two, while that in singular models is smaller and varies in learning models. The learning coefficient is known mathematically as the log canonical threshold. In this paper, we provide a new rational blowing-up method for obtaining these coefficients. In the application to Vandermonde matrix-type singularities, we show the efficiency of such methods.

scholarly journals A comparison of Bayesian and frequentist model selection methods for factor analysis models.

2017 ◽  
Vol 22 (2) ◽  
pp. 361-381 ◽  
Author(s):  
Zhao-Hua Lu ◽  
Sy-Miin Chow ◽  
Eric Loken
Keyword(s):  
Factor Analysis ◽  
Model Selection ◽  
Selection Methods ◽  
Analysis Models

The Performance of IRT Model Selection Methods With Mixed-Format Tests

2012 ◽  
Vol 36 (3) ◽  
pp. 159-180 ◽  
Author(s):  
Tiffany A. Whittaker ◽  
Wanchen Chang ◽  
Barbara G. Dodd
Keyword(s):  
Model Selection ◽  
Selection Methods ◽  
Irt Model ◽  
Mixed Format ◽  
Mixed Format Tests

scholarly journals Application of model selection methods for the identification of a dynamic boost pressure model

2015 ◽  
Vol 48 (28) ◽  
pp. 829-834
Author(s):  
M. Kahl ◽  
A. Kroll ◽  
R. Kästner ◽  
M. Sofsky
Keyword(s):  
Model Selection ◽  
Boost Pressure ◽  
Selection Methods
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