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Sökning: WFRF:(PAUL C) > Rapport

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  • Flannagan, Carol A.C., et al. (författare)
  • Mutual Recognition Methodology Development
  • 2015
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • Phase 1 of the Mutual Recognition Methodology Development (MRMD) project developed an approach to statistical modeling and analysis of field data to address the state of evidence relevant to mutual recognition of automotive safety regulations. Specifically, the report describes a methodology that can be used to measure evidence for the hypothesis that vehicles meeting EU safety standards would perform similarly to US-regulated vehicles in the US driving environment, and that vehicles meeting US safety standards would perform similarly to EU-regulated vehicles in the EU driving environment. As part of the project, we assessed the availability and contents of crash datasets from the US and the EU, as well as their collective ability to support the proposed statistical methodology.The report describes a set of three statistical approaches to “triangulate” evidence regarding similarity or differences in crash and injury risk associated with EU- and US-regulated vehicles. Approach 1, Seemingly Unrelated Regression, tests whether the models are identical and will also assess the capability of the data analysis to detect differences in the models, if differences exist.Approach 2, Consequences of Best Models, uses logistic regression to develop two separate models, one for EU risk and one for US risk, as a function of a set of predictors (i.e., crash, vehicle, and occupant conditions). The two models will then be exercised on a standard population for the EU and a standard population for the US. Approach 3, Evidence for Consequences, turns the question aroundto measures the overall evidence for each of a set of possible conclusions. Each conclusion is characterized by a range of relative risk on a single population. Evidence is measured using a weighted average of likelihoods for a large group of models that produce the same outcome. That evidence is then compared using Bayes Factors.
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  • Toth, Roland, et al. (författare)
  • Order and Structural Dependence Selection of LPV-ARX Models using a Nonnegative Garrote Approach
  • 2010
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.
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