All Outputs (5)

Marginal and joint reliability importance based on survival signature (2017)
Journal Article
Eryilmaz, S., Coolen, F., & Coolen-Maturi, T. (2018). Marginal and joint reliability importance based on survival signature. Reliability Engineering & System Safety, 172, 118-128. https://doi.org/10.1016/j.ress.2017.12.002

Marginal and joint reliability importance measures have been found to be useful in optimal system design. Various importance measures have been defined and studied for a variety of system models. The results in the literature are mostly based on the... Read More about Marginal and joint reliability importance based on survival signature.

Nonparametric predictive inference for future order statistics (2017)
Journal Article
Coolen, F., Coolen-Maturi, T., & Alqifari, H. (2018). Nonparametric predictive inference for future order statistics. Communications in Statistics - Theory and Methods, 47(10), 2527-2548. https://doi.org/10.1080/03610926.2017.1342834

This paper presents nonparametric predictive inference for future order statistics. Given data consisting of n real-valued observations, m future observations are considered and predictive probabilities are presented for the r-th ordered future obser... Read More about Nonparametric predictive inference for future order statistics.

An imprecise statistical method for accelerated life testing using the power-Weibull model (2017)
Journal Article
Yin, Y., Coolen, F., & Coolen-Maturi, T. (2017). An imprecise statistical method for accelerated life testing using the power-Weibull model. Reliability Engineering & System Safety, 167, 158-167. https://doi.org/10.1016/j.ress.2017.05.045

Accelerated life testing provides an interesting challenge for quantification of the uncertainties involved, in particular due to the required linking of the units’ failure times, or failure time distributions, at different stress levels. This paper... Read More about An imprecise statistical method for accelerated life testing using the power-Weibull model.

Mixture Models for Prediction from Time Series, with Application to Energy Use Data (2017)
Journal Article
Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2017). Mixture Models for Prediction from Time Series, with Application to Energy Use Data. Archives of data science. Series A, 2(1), 1-15. https://doi.org/10.5445/ksp/1000058749/07

This paper aims to use mixture models to produce predictions from time series data. Given data of the form (ti, yi), i = 1, . . . , T , we propose a mix- ture model localized at time point tT with the k-th component as yi = mk (ti) + εik with mixing... Read More about Mixture Models for Prediction from Time Series, with Application to Energy Use Data.

Simulation Methods for System Reliability Using the Survival Signature (2017)
Journal Article
Patelli, E., Feng, G., Coolen, F., & Coolen-Maturi, T. (2017). Simulation Methods for System Reliability Using the Survival Signature. Reliability Engineering & System Safety, 167, 327-337. https://doi.org/10.1016/j.ress.2017.06.018

Recently, the survival signature has been presented as a summary of the structure function which is sufficient for computation of common reliability metrics and has the crucial advantage that it can be applied to systems with components whose failure... Read More about Simulation Methods for System Reliability Using the Survival Signature.