A Copula-Based Approach for Model Bias Characterization

Zhimin Xi, Pan Hao, Yan Fu, Ren Jye Yang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

Original languageEnglish (US)
Pages (from-to)781-786
Number of pages6
JournalSAE International Journal of Passenger Cars - Mechanical Systems
Volume7
Issue number2
DOIs
StatePublished - Aug 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Mechanical Engineering

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