Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/128009
Title: A discrete mixture regression for modeling the duration of non-hospitalization medical leave of motor accident victims
Author: Bermúdez, Lluís
Karlis, Dimitris
Santolino, Miguel
Keywords: Accidents de trànsit
Anàlisi de regressió
Risc (Assegurances)
Discapacitats
Traffic accidents
Regression analysis
Risk (Insurance)
People with disabilities
Issue Date: Dec-2018
Publisher: Elsevier
Abstract: Studies analyzing the temporary repercussions of motor vehicle accidents are scarcer than those analyzing permanent injuries or mortality. A regression model to evaluate the risk factors affecting the duration of temporary disability after injury in such an accident is constructed using a motor insurance dataset. The length of non-hospitalization medical leave, measured in days, following a motor accident is used here as a measure of the severity of temporary disability. The probability function of the number of days of sick leave presents spikes in multiples of five (working week), seven (calendar week) and thirty (month), etc. To account for this, a regression model based on finite mixtures of multiple discrete distributions is proposed to fit the data properly. The model provides a very good fit when the multiples for the working week, week, fortnight and month are taken into account. Victim characteristics of gender and age and accident characteristics of the road user type, vehicle class and the severity of permanent injuries were found to be significant when accounting for the duration of temporary disability.
Note: Versió postprint del document publicat a: https://doi.org/10.1016/j.aap.2018.09.006
It is part of: Accident Analysis and Prevention, 2018, vol. 121, num. December, p. 157-165
URI: http://hdl.handle.net/2445/128009
Related resource: https://doi.org/10.1016/j.aap.2018.09.006
ISSN: 0001-4575
Appears in Collections:Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

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