A Bayesian finite mixture change-point model for assessing the risk of novice teenage drivers

DOI10.1080/02664763.2017.1288202OpenAlexW2587440683WikidataQ49232461 ScholiaQ49232461MaRDI QIDQ5139021

Bruce Simons-Morton, Feng Guo, Qing Li, Inyoung Kim, Sheila G. Klauer

Publication date: 4 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5784858

zbMATH Keywords

recurrent eventschange-point modelslatent class modelsnaturalistic driving studyteenage driving risk

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

A non-parametric Bayesian change-point method for recurrent events ⋮ A change-point detection and clustering method in the recurrent-event context ⋮ Bayesian change-points detection assuming a power law process in the recurrent-event context ⋮ A Bayesian Time-Varying Coefficient Model for Multitype Recurrent Events

Uses Software

• GASP
• BayesDA
• PMTK

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