Outputs (144)

Smoothed bootstrap methods for bivariate data (2023)
Journal Article
Al Luhayb, A., Coolen-Maturi, T., & Coolen, F. (2023). Smoothed bootstrap methods for bivariate data. Journal of statistical theory and practice, 17(3), Article 37. https://doi.org/10.1007/s42519-023-00334-7

A smoothed bootstrap method is introduced for right-censored data based on the rightcensoring-A(n) assumption introduced by Coolen and Yan (2004), which is a generalization of Hill’s A(n) assumption (Hill, 1968) for right-censored data. The smoothed... Read More about Smoothed bootstrap methods for bivariate data.

A generalized system reliability model based on survival signature and multiple competing failure processes (2023)
Journal Article
Chang, M., Coolen, F., Coolen-Maturi, T., & Huang, X. (online). A generalized system reliability model based on survival signature and multiple competing failure processes. Journal of Computational and Applied Mathematics, 435, Article 115364. https://doi.org/10.1016/j.cam.2023.115364

Degradation-based system reliability analysis has been extensively conducted, but the components in a system are assumed to experience similar degradation and shock processes, neglecting actual failure mechanisms. However, multiple types of component... Read More about A generalized system reliability model based on survival signature and multiple competing failure processes.

New reliability model for complex systems based on stochastic processes and survival signature (2023)
Journal Article
Chang, M., Huang, X., Coolen, F., & Coolen-Maturi, T. (2023). New reliability model for complex systems based on stochastic processes and survival signature. European Journal of Operational Research, 309(3), 1349-1364. https://doi.org/10.1016/j.ejor.2023.02.027

For systems with complicated structures, reliability analysis based on survival signature has been carried out by modelling time-to-failure data with specific distributions. However, for highly reliable systems, only little or no failure data may be... Read More about New reliability model for complex systems based on stochastic processes and survival signature.

Smoothed Bootstrap for Right-Censored Data (2023)
Journal Article
Luhayb, A. S. M. A., Coolen, F. P., & Coolen-Maturi, T. (2024). Smoothed Bootstrap for Right-Censored Data. Communications in Statistics - Theory and Methods, 53(11), 4037-4061. https://doi.org/10.1080/03610926.2023.2171708

A smoothed bootstrap method is introduced for right-censored data based on the right-censoring-A(n) assumption introduced by Coolen and Yan, which is a generalization of Hill’s A(n) assumption for right-censored data. The smoothed bootstrap method is... Read More about Smoothed Bootstrap for Right-Censored Data.

Logic Differential Calculus for Reliability Analysis Based on Survival Signature (2022)
Journal Article
Rusnak, P., Zaitseva, E., Coolen, F., Kvassay, M., & Levashenko, V. (2023). Logic Differential Calculus for Reliability Analysis Based on Survival Signature. IEEE Transactions on Dependable and Secure Computing, 20(2), 1529-1540. https://doi.org/10.1109/tdsc.2022.3159126

The structure function is an often-used mathematical representation of the investigated system in reliability analysis. It is a binary function that models system state according to states of its components. The size of the structure function depends... Read More about Logic Differential Calculus for Reliability Analysis Based on Survival Signature.

Discussion of signature‐based models of preventive maintenance (2022)
Journal Article
Coolen, F. P., Coolen‐Maturi, T., & van Houtum, G. (2023). Discussion of signature‐based models of preventive maintenance. Applied Stochastic Models in Business and Industry, 39(1), 68-70. https://doi.org/10.1002/asmb.2716

As a contribution to the discussion of the paper An overview of some classical models and discussion of the signature-based models of preventive maintenance,1 we consider the assumption of exchangeability of the failure times of components in systems... Read More about Discussion of signature‐based models of preventive maintenance.

A Cost-Sensitive Imprecise Credal Decision Tree based on Nonparametric Predictive Inference (2022)
Journal Article
Moral-Garcia, S., Abellan, J., Coolen-Maturi, T., & Coolen, F. (2022). A Cost-Sensitive Imprecise Credal Decision Tree based on Nonparametric Predictive Inference. Applied Soft Computing, 123, Article 108916. https://doi.org/10.1016/j.asoc.2022.108916

Classifiers sometimes return a set of values of the class variable since there is not enough information to point to a single class value. These classifiers are known as imprecise classifiers. Decision Trees for Imprecise Classification were proposed... Read More about A Cost-Sensitive Imprecise Credal Decision Tree based on Nonparametric Predictive Inference.

Pricing exotic options in the incomplete market: an imprecise probability method (2022)
Journal Article
He, T., Coolen, F., & Coolen-Maturi, T. (2022). Pricing exotic options in the incomplete market: an imprecise probability method. Applied Stochastic Models in Business and Industry, 38(3), 422-440. https://doi.org/10.1002/asmb.2668

This paper considers a novel exotic option pricing method for incomplete markets. Nonparametric Predictive Inference (NPI) is applied to the option pricing procedure based on the binomial tree model allowing the method to evaluate exotic options with... Read More about Pricing exotic options in the incomplete market: an imprecise probability method.

Uncertainty in Engineering - Introduction to Methods and Applications (2021)
Book
Aslett, L., Coolen, F., & De Bock, J. (Eds.). (2022). Uncertainty in Engineering - Introduction to Methods and Applications. Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability th... Read More about Uncertainty in Engineering - Introduction to Methods and Applications.

Counterfactual explanation of machine learning survival models (2021)
Journal Article
Kovalev, M., Utkin, L., Coolen, F., & Konstantinov, A. (2022). Counterfactual explanation of machine learning survival models. Informatica: An International Journal, 32(4), 817-847. https://doi.org/10.15388/21-infor468

A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine le... Read More about Counterfactual explanation of machine learning survival models.