Analysis of train derailment severity using vine copula quantile regression modeling-Reference-Cited by-同舟云学术

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Analysis of train derailment severity using vine copula quantile regression modeling

Published:2019-08 Issue: Volume:105 Page:485-503
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Martey Emmanuel Nii,Attoh-Okine Nii

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference67 articles.

1. Pair-copula constructions of multiple dependence;Aas;Insur.: Math. Econ.,2009

2. Beyond simplified pair-copula constructions;Acar;J. Multivariate Anal.,2012

3. Prediction of copper recovery from geometallurgical data using D-vine copulas;Addo,2018

4. A new look at the statistical model identification;Akaike;IEEE Trans. Autom. Control,1974

5. Derailment probability analysis and modeling of mainline freight trains;Anderson,2005

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1. Modelling credit card exposure at default using vine copula quantile regression;European Journal of Operational Research;2023-11

2. Analyzing the Severity of Freight Train Derailment Using Choquet Integral-Based Fuzzy Measure Weight Regression;2023 IEEE 19th International Conference on Automation Science and Engineering (CASE);2023-08-26

3. Statistical Analysis of Train Derailment Severity for Unit Trains Versus Manifest Trains;Transportation Research Record: Journal of the Transportation Research Board;2023-07-20

4. Data-driven decision support system for degrading assets and its application under the perspective of a railway component;Transportation Engineering;2023-06

5. Artificial intelligence in railway infrastructure: current research, challenges, and future opportunities;Intelligent Transportation Infrastructure;2023

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