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An algorithm to fit conditional tail expectation regression models for vehicle excess speed in driving data

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An algorithm to fit conditional tail expectation regression models for vehicle excess speed in driving data

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Pitarque, A.; Guillen, M. (2020). An algorithm to fit conditional tail expectation regression models for vehicle excess speed in driving data. Editorial Universitat Politècnica de València. 51-58. https://doi.org/10.4995/CARMA2020.2020.11512

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/148773

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Title: An algorithm to fit conditional tail expectation regression models for vehicle excess speed in driving data
Author: Pitarque, Albert Guillen, Montserrat
Issued date:
Abstract:
[EN] An algorithm to fit regression models aimed at predicted the average responses beyond a conditional quantile level is presented. This procedure is implemented in a case study of insured drivers covering almost 10,000. ...[+]
Subjects: Web data , Internet data , Big data , Qca , Pls , Sem , Conference , Telematics , Quantile regression , Insurance , Tail value-at-risk , Traffic safety
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
ISBN: 9788490488324
DOI: 10.4995/CARMA2020.2020.11512
Publisher:
Editorial Universitat Politècnica de València
Publisher version: http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/view/11512
Conference name: CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics
Conference place: Valencia, Spain
Conference date: Julio 08-09,2020
Type: Capítulo de libro Comunicación en congreso

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