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Bazgan Cristina Beaujean Paul Gourdin Éric 《Journal of Combinatorial Optimization》2022,44(3):1880-1899
Journal of Combinatorial Optimization - Modifying the topology of a network to mitigate the spread of an epidemic with epidemiological constant $$\lambda $$ amounts to the NP-hard problem of... 相似文献
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Brandi N. Falley James D. Stamey A. Alexander Beaujean 《Journal of applied statistics》2018,45(10):1756-1769
Measurement error is a commonly addressed problem in psychometrics and the behavioral sciences, particularly where gold standard data either does not exist or are too expensive. The Bayesian approach can be utilized to adjust for the bias that results from measurement error in tests. Bayesian methods offer other practical advantages for the analysis of epidemiological data including the possibility of incorporating relevant prior scientific information and the ability to make inferences that do not rely on large sample assumptions. In this paper we consider a logistic regression model where both the response and a binary covariate are subject to misclassification. We assume both a continuous measure and a binary diagnostic test are available for the response variable but no gold standard test is assumed available. We consider a fully Bayesian analysis that affords such adjustments, accounting for the sources of error and correcting estimates of the regression parameters. Based on the results from our example and simulations, the models that account for misclassification produce more statistically significant results, than the models that ignore misclassification. A real data example on math disorders is considered. 相似文献
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