@inproceedings{ce5bd3c3b1c2440b84f9fa4c7695cddb,
title = "Multi-dimensional failure probability estimation in automotive industry based on censored warranty data",
abstract = "The warranty datasets available for various car models are characterized by extremely imbalanced classes, where a very low amount of under-warranty vehicles have at least one matching claim ({"}failure{"}) of a given type. The failure probability estimation becomes even more complex in the presence of censored warranty data, where some of the vehicles have not reached yet the upper limit of the predicted interval. The actual mileage rate of under-warranty vehicles is another source of uncertainty in warranty datasets. In this paper, we present a new, continuous-time methodology for failure probability estimation from multi-dimensional censored datasets in automotive industry.",
keywords = "Automotive Industry, censored data, multi-dimensional failure prediction, warranty data",
author = "Mark Last and Alexandra Zhmudyak and Hezi Halpert and Sugato Chakrabarty",
year = "2013",
month = jan,
day = "1",
doi = "10.1007/978-3-642-33042-1_54",
language = "English",
isbn = "9783642330414",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "507--515",
booktitle = "Synergies of Soft Computing and Statistics for Intelligent Data Analysis",
address = "Germany",
note = "6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012 ; Conference date: 04-10-2012 Through 06-10-2012",
}
