Outputs (6)

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.

Statistical reproducibility for pairwise t-tests in pharmaceutical research (2021)
Journal Article
Simkus, A., Coolen, F., Coolen-Maturi, T., Karp, N., & Bendtsen, C. (2022). Statistical reproducibility for pairwise t-tests in pharmaceutical research. Statistical Methods in Medical Research, 31(4), 673-688. https://doi.org/10.1177/09622802211041765

This paper investigates statistical reproducibility of the t-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference (NPI) method. Within our research framework, statistical reproducibilit... Read More about Statistical reproducibility for pairwise t-tests in pharmaceutical research.

Survival signature for reliability evaluation of a multi-state system with multi-state components (2021)
Journal Article
Qin, J., & Coolen, F. (2022). Survival signature for reliability evaluation of a multi-state system with multi-state components. Reliability Engineering & System Safety, 218(Part A), Article 108129. https://doi.org/10.1016/j.ress.2021.108129

Survival signature technology has recently attracted increasing attention for its merits on quantifying reliability of systems with multiple types of components. In order to implement reliability evaluation of multi-state system (MSS), computing meth... Read More about Survival signature for reliability evaluation of a multi-state system with multi-state components.

Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels (2021)
Journal Article
Chang, M., Huang, X., Coolen, F., & Coolen-Maturi, T. (2021). Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels. Reliability Engineering & System Safety, 216, Article 108007. https://doi.org/10.1016/j.ress.2021.108007

Degradation-shock failure processes widely exist in practice, and extensive work has been carried out to better describe such processes. In this paper, a new model is developed for reliability analysis of systems subject to dependent degradation-shoc... Read More about Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels.

A monotonicity property of weighted log-rank tests (2021)
Journal Article
Coolen-Maturi, T., & Coolen, F. (2023). A monotonicity property of weighted log-rank tests. Communications in Statistics - Theory and Methods, 52(7), 2402-2416. https://doi.org/10.1080/03610926.2021.1952270

The logrank test is a well-known nonparametric test which is often used to compare the survival distributions of two samples including right-censored observations, it is also known as the Mantel-Haenszel test. The Gρ family of tests, generalizes the... Read More about A monotonicity property of weighted log-rank tests.

Imprecise inference based on the log-rank test for accelerated life testing (2021)
Journal Article
Coolen, F., Ahmadini, A., & Coolen-Maturi, T. (2021). Imprecise inference based on the log-rank test for accelerated life testing. Metrika, 84, 913-925. https://doi.org/10.1007/s00184-021-00807-4

This paper presents an imprecise predictive inference method for accelerated life testing. The method is largely nonparametric, with a basic parametric function to link different stress levels. The log-rank test is used to provide imprecision for the... Read More about Imprecise inference based on the log-rank test for accelerated life testing.