@misc{10481/88039,
year = {2021},
url = {https://hdl.handle.net/10481/88039},
abstract = {In this article, we build a statistical model able to predict the reliability of the system based on a dataset. Our objective
is double. On the one hand, we aim at constructing a function that classifies the system in one of the two categories (operative
or failed) based on the knowledge of components states. On the other hand, we present a statistical test to decide the order of
importance of components in terms of the effect each one has on the system performance. We present a supervised algorithm
involving isotonic smooth logistic regression and cross-validation techniques. Our method is completely data-driven not lying in
any parametric assumptions. The method is illustrated through an extensive simulation study.},
publisher = {IEEE},
title = {A Machine Learning Algorithm for Reliability Analysis},
doi = {10.1109/TR.2020.3011653},
author = {Gámiz Pérez, María Luz and Navas Gómez, Fernando Jesús and Raya Miranda, Rocío},
}