Outputs (143)

Non-linear failure rate: A Bayes study using Hamiltonian Monte Carlo simulation (2020)
Journal Article
Thach, T., Bris, R., Volf, P., & Coolen, F. (2020). Non-linear failure rate: A Bayes study using Hamiltonian Monte Carlo simulation. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 123, 55-76. https://doi.org/10.1016/j.ijar.2020.04.007

A generalization of the linear failure rate called non-linear failure rate is introduced, analyzed, and applied to real data sets for both censored and uncensored data. The Hamiltonian Monte Carlo and cross-entropy methods have been exploited to empo... Read More about Non-linear failure rate: A Bayes study using Hamiltonian Monte Carlo simulation.

Nonparametric Predictive Inference for American Option Pricing based on the Binomial Tree Model (2020)
Journal Article
He, T., Coolen, F., & Coolen-Maturi, T. (2021). Nonparametric Predictive Inference for American Option Pricing based on the Binomial Tree Model. Communications in Statistics - Theory and Methods, 50(20), 4657-4684. https://doi.org/10.1080/03610926.2020.1764040

In this article, we present the American option pricing procedure based on the binomial tree from an imprecise statistical aspect. Nonparametric Predictive Inference (NPI) is implemented to infer imprecise probabilities of underlying asset movements,... Read More about Nonparametric Predictive Inference for American Option Pricing based on the Binomial Tree Model.

Estimation of small area total with randomized data (2020)
Journal Article
Ahmed, S., Shabbir, J., Gupta, S., & Coolen, F. (2020). Estimation of small area total with randomized data. Revstat Statistical Journal, 18(2), 223-235

In social surveys involving questions that are sensitive or personal in nature, respondents may not provide correct answers to certain questions asked by the interviewer. The impact of this nonresponse or inaccurate response becomes even more acute i... Read More about Estimation of small area total with randomized data.

Imprecise weighted extensions of random forests for classification and regression (2020)
Journal Article
Utkin, L., Kovalev, M., & Coolen, F. (2020). Imprecise weighted extensions of random forests for classification and regression. Applied Soft Computing, 92, Article 106324. https://doi.org/10.1016/j.asoc.2020.106324

One of the main problems of using the random forests (RF) in classification and regression tasks is a lack of sufficient data which fall into certain leaves of trees in order to estimate the tree predicted values. To cope with this problem, robust im... Read More about Imprecise weighted extensions of random forests for classification and regression.

Reliability analysis of phased mission systems when components can be swapped upon failure (2020)
Journal Article
Coolen, F., Huang, X., & Najem, A. (2020). Reliability analysis of phased mission systems when components can be swapped upon failure. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(2), Article 020905. https://doi.org/10.1115/1.4046328

This paper proposes a new strategy to improve the reliability of phased mission systems (PMS), namely, by swapping of components. In the proposed strategy, when a component fails, it can be swapped by another one in the system which is still function... Read More about Reliability analysis of phased mission systems when components can be swapped upon failure.

A new boosting-based software reliability growth model (2020)
Journal Article
Utkin, L., & Coolen, F. (2021). A new boosting-based software reliability growth model. Communications in Statistics - Theory and Methods, 50(24), 6167-6194. https://doi.org/10.1080/03610926.2020.1740736

A new software reliability growth model (SRGM) called RBoostSRGM is proposed in this paper. It can be regarded as a modification of the boosting SRGMs through the use of a reduced set of weights to take into account the behavior of the software relia... Read More about A new boosting-based software reliability growth model.

Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature (2020)
Journal Article
Li, Y., Coolen, F., Zhu, C., & Tan, J. (2020). Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature. Renewable Energy, 153, 766-776. https://doi.org/10.1016/j.renene.2020.02.017

The hydraulic system is one of the most critical subsystems of wind turbines. It is used to reset the aerodynamic brakes. Because of this, the reliability of the hydraulic system is important to the functioning of the entire wind turbine. To realisti... Read More about Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature.

Nonparametric predictive inference for comparison of two diagnostic tests (2020)
Journal Article
Alabdulhadi, M., Coolen-Maturi, T., & Coolen, F. (2021). Nonparametric predictive inference for comparison of two diagnostic tests. Communications in Statistics - Theory and Methods, 50(19), 4470-4486. https://doi.org/10.1080/03610926.2020.1719157

An important aim in diagnostic medical research is comparison of the accuracy of two diagnostic tests. In this paper, comparison of two diagnostic tests is presented using nonparametric predictive inference (NPI) for future order statistics. The test... Read More about Nonparametric predictive inference for comparison of two diagnostic tests.

Statistical inference for the Arrhenius-Weibull accelerated life testing model with imprecision based on the likelihood ratio test (2019)
Journal Article
Ahmadini, A., & Coolen, F. (2020). Statistical inference for the Arrhenius-Weibull accelerated life testing model with imprecision based on the likelihood ratio test. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(2), 275-289. https://doi.org/10.1177/1748006x19884860

In this paper, we present a new imprecise statistical inference method for accelerated life testing data, where nonparametric predictive inferences at normal stress levels are integrated with a parametric Arrhenius-Weibull model. The method includes... Read More about Statistical inference for the Arrhenius-Weibull accelerated life testing model with imprecision based on the likelihood ratio test.

On nonparametric predictive inference for asset and European option trading in the binomial tree model (2019)
Journal Article
Chen, J., Coolen, F., & Coolen-Maturi, T. (2019). On nonparametric predictive inference for asset and European option trading in the binomial tree model. Journal of the Operational Research Society, 70(10), 1678-1691. https://doi.org/10.1080/01605682.2019.1643682

This paper introduces a novel method for asset and option trading in a binomial scenario. This method uses nonparametric predictive inference (NPI), a statistical methodology within im- precise probability theory. Instead of inducing a single probabi... Read More about On nonparametric predictive inference for asset and European option trading in the binomial tree model.