Publication de 6 numéros par an
ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099
Indexed in
BAYESIAN NONPARAMETRIC GENERAL REGRESSION
RÉSUMÉ
Bayesian identification has attracted considerable interest in various research areas for the determination of the mathematical model with suitable complexity based on input-output measurements. Regression analysis is an important tool in which Bayesian inference and Bayesian model selection have been applied. However, it has been noted that there is a subjectivity problem of model selection results due to the assignment of the prior distribution of the regression coefficients. Since regression coefficients are not physical parameters, assignment of their prior distribution is nontrivial. To resolve this problem, we propose a novel nonparametric regression method using Bayesian model selection in conjunction with general regression. In order to achieve this goal, we also reformulate the general regression under the Bayesian framework. There are two attractive features of the proposed method. First, it eliminates the subjectivity of model selection results due to the prior distribution of the regression coefficients. Second, the number of model candidates is drastically reduced, compared with traditional regression using the same number of design/input variables. Therefore, this allows for the consideration of a much larger number of potential design variables. The proposed method will be assessed and validated through two simulated examples and two real applications.
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Wang Yu, Akeju Oluwatosin Victor, Zhao Tengyuan, Interpolation of spatially varying but sparsely measured geo-data: A comparative study, Engineering Geology, 231, 2017. Crossref
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Yuen Ka-Veng, Ortiz Gilberto A., Multiresolution Bayesian nonparametric general regression for structural model updating, Structural Control and Health Monitoring, 25, 2, 2018. Crossref
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Kuok Sin‐Chi, Yuen Ka‐Veng, Broad learning for nonparametric spatial modeling with application to seismic attenuation, Computer-Aided Civil and Infrastructure Engineering, 35, 3, 2020. Crossref
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Phoon Kok-Kwang, The story of statistics in geotechnical engineering, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 14, 1, 2020. Crossref
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Zhou Wan-Huan, Yin Zhen-Yu, Yuen Ka-Veng, Selection of Physical and Chemical Properties of Natural Fibers for Predicting Soil Reinforcement, in Practice of Bayesian Probability Theory in Geotechnical Engineering, 2021. Crossref
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Garoli Gabriel Y., Pilotto Rafael, Nordmann Rainer, de Castro Helio F., Identification of active magnetic bearing parameters in a rotor machine using Bayesian inference with generalized polynomial chaos expansion, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43, 12, 2021. Crossref
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Zhou Yuanyuan, Qin Nianxiu, Tang Qiuhong, Shi Huabin, Gao Liang, Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework, Remote Sensing, 13, 6, 2021. Crossref
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Zhao Lin-Shuang, Zhou Wan-Huan, Su Li-Jun, Garg Ankit, Yuen Ka-Veng, Selection of Physical and Chemical Properties of Natural Fibers for Predicting Soil Reinforcement, Journal of Materials in Civil Engineering, 31, 10, 2019. Crossref
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Ding Zhi, Zhao Lin-Shuang, Zhou Wan-Huan, Bezuijen Adam, Intelligent Prediction of Multi-Factor-Oriented Ground Settlement During TBM Tunneling in Soft Soil, Frontiers in Built Environment, 8, 2022. Crossref
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Kuok Sin-Chi, Yuen Ka-Veng, Bayesian Nonparametric Modeling of Structural Health Indicators under Severe Typhoons and Its Application to Modeling Modal Frequency, Journal of Aerospace Engineering, 32, 4, 2019. Crossref
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Wang Sheng, Yuen Ka-Veng, Yang Xiaofeng, Zhang Biao, A Nonparametric Tropical Cyclone Wind Speed Estimation Model Based on Dual-Polarization SAR Observations, IEEE Transactions on Geoscience and Remote Sensing, 60, 2022. Crossref