Journal article
An efficient copula-based method of identifying regression models of non-monotonic relationships in processing plants
Chemical engineering science, v 136
02 Nov 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper presents an efficient semi-parametric method of identifying regression models based on a parametric copula-based joint probability distribution of input and output variables. This regression-model identification method uses our recently-introduced rolling pin method of estimating joint probability distributions, and therefore it can model highly nonlinear and nonmonotonic relationships. As the proposed method does not require any assumptions on the homoskedasticity, it can model relationships with noise terms whose variances depend on input variables. Moreover, it allows the user to calculate confidence intervals of the identified regression model through the estimated joint probability distribution. As the model-training computational cost increases quadratically with the number of variables, the method is suitable for large-scale applications such as modeling relationships in industrial processing plants. The application and performance of the proposed method are shown using two examples. (C) 2015 Elsevier Ltd. All rights reserved.
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Details
- Title
- An efficient copula-based method of identifying regression models of non-monotonic relationships in processing plants
- Creators
- Taha Mohseni Ahooyi - Drexel UniversityJeffrey E. Arbogast - Air LiquideMasoud Soroush - Drexel University
- Publication Details
- Chemical engineering science, v 136
- Publisher
- Elsevier
- Number of pages
- 9
- Grant note
- CBET-1066461 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000361408600010
- Scopus ID
- 2-s2.0-84929239245
- Other Identifier
- 991019168971304721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Engineering, Chemical