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Outputs (9)

Optimisation of maintenance policy under parameter uncertainty using portfolio theory (2016)
Journal Article
Wu, S., Coolen, F., & Liu, B. (2017). Optimisation of maintenance policy under parameter uncertainty using portfolio theory. IISE Transactions, 49(7), 711-721. https://doi.org/10.1080/24725854.2016.1267881

In reliability mathematics, optimisation of maintenance policy is derived based on reliability indexes such as the reliability or its derivatives (e.g., the cumulative failure intensity or the renewal function) and the associated cost information. Th... Read More about Optimisation of maintenance policy under parameter uncertainty using portfolio theory.

On the structure function and survival signature for system reliability (2016)
Journal Article
Coolen, F., & Coolen-Maturi, T. (2016). On the structure function and survival signature for system reliability. Safety and Reliability, 36(2), 77-87. https://doi.org/10.1080/09617353.2016.1219936

The quantification of reliability of systems has, for decades, been based on the structure function, which expresses functioning of a system given the states of its components. One problem of the structure function is that, in its simplest form, for... Read More about On the structure function and survival signature for system reliability.

Bayesian nonparametric system reliability using sets of priors (2016)
Journal Article
Walter, G., Aslett, L., & Coolen, F. (2017). Bayesian nonparametric system reliability using sets of priors. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 80(1), 67-88. https://doi.org/10.1016/j.ijar.2016.08.005

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the f... Read More about Bayesian nonparametric system reliability using sets of priors.

Nonparametric predictive inference for stock returns (2016)
Journal Article
Baker, R., Coolen-Maturi, T., & Coolen, F. (2017). Nonparametric predictive inference for stock returns. Journal of Applied Statistics, 44(8), 1333-1349. https://doi.org/10.1080/02664763.2016.1204429

In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which d... Read More about Nonparametric predictive inference for stock returns.

The structure function for system reliability as predictive (imprecise) probability (2016)
Journal Article
Coolen, F., & Coolen-Maturi, T. (2016). The structure function for system reliability as predictive (imprecise) probability. Reliability Engineering & System Safety, 154, 180-187. https://doi.org/10.1016/j.ress.2016.06.008

In system reliability, the structure function models functioning of a system for given states of its components. As such, it is typically a straightforward binary function which plays an essential role in reliability assessment, yet it has received r... Read More about The structure function for system reliability as predictive (imprecise) probability.

k-Boxplots for mixture data (2016)
Journal Article
Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2018). k-Boxplots for mixture data. Statistical Papers, 59(2), 513-528. https://doi.org/10.1007/s00362-016-0774-7

This article introduces a new graphical tool to summarize data which possess a mixture structure. Computation of the required summary statistics makes use of posterior probabilities of class membership which can be obtained from a fitted mixture mode... Read More about k-Boxplots for mixture data.

Predictive inference for bivariate data: Combining nonparametric predictive inference for marginals with an estimated copula (2016)
Journal Article
Coolen-Maturi, T., Coolen, F., & Muhammad, N. (2016). Predictive inference for bivariate data: Combining nonparametric predictive inference for marginals with an estimated copula. Journal of statistical theory and practice, 10(3), 515-538. https://doi.org/10.1080/15598608.2016.1184112

This paper presents a new method for prediction of an event involving a future bivariate observation. The method combines nonparametric predictive inference (NPI) applied to the marginals with a parametric copula to model and estimate the dependence... Read More about Predictive inference for bivariate data: Combining nonparametric predictive inference for marginals with an estimated copula.

Imprecise system reliability and component importance based on survival signature (2016)
Journal Article
Feng, G., Patelli, E., Beer, M., & Coolen, F. (2016). Imprecise system reliability and component importance based on survival signature. Reliability Engineering & System Safety, 150, 116-125. https://doi.org/10.1016/j.ress.2016.01.019

The concept of the survival signature has recently attracted increasing attention for performing reliability analysis on systems with multiple types of components. It opens a new pathway for a structured approach with high computational efficiency ba... Read More about Imprecise system reliability and component importance based on survival signature.