[1] M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, ... &
S. Nahavandi, A review of uncertainty quantification in deep learning: Techniques, applications and challenges, in *Information Fusion*, 76, 243–297, (2021).
[2] S. Cheng, C. Quilodran-Casas, S. Ouala, A. Farchi, C. Liu, P. Tandeo, ... & R. Ar- ´
cucci, Machine learning with data assimilation and uncertainty quantification for dynamical
systems: a review, in *IEEE/CAA Journal of Automatica Sinica*, 10(6), 1361–1387, (2023).
[3] K. Wang, C. Shen, X. Li, & J. Lu, Uncertainty quantification for safe and reliable autonomous vehicles: A review of methods and applications, in *IEEE Transactions on Intelligent Transportation Systems*, (2025).
[4] P. Zhang, S. Liu, D. Lu, G. Zhang, & R. Sankaran, A prediction interval method for
uncertainty quantification of regression models, Oak Ridge National Lab.(ORNL), Oak Ridge,
TN, (2021).
[5] E. Nikulchev & A. Chervyakov, Prediction intervals: A geometric view, in *Symmetry*,
15(4), 781, (2023).
[6] Y. Cui & M. G. Xie, Confidence distribution and distribution estimation for modern statistical inference, in *Springer Handbook of Engineering Statistics*, 575–592, London: Springer
London, (2023).
[7] C. Thiele & G. Hirschfeld, Confidence intervals and sample size planning for optimal
cutpoints, in *PLOS One*, 18(1), e0279693, (2023).
[8] A. Ganguly & T. Sutter, Optimal learning via moderate deviations theory, arXiv preprint
arXiv:2305.14496, (2023).
[9] W. Wu, T. Zou, L. Zhang, K. Wang, & X. Li, Similarity-Based Remaining Useful Lifetime
Prediction Method Considering Epistemic Uncertainty, in *Sensors*, 23(23), 9535, (2023).
[10] Q. Wei, R. Wang, & C. Y. Ruan, Similarity Measures of Probabilistic Interval Preference
Ordering Sets and Their Applications in Decision-Making, in *Mathematics*, 12(20), 3255,
(2024).
[11] Y. Zhao, Y. Wang, Z. Wang, & J. Wang, The sub-interval similarity: A general uncertainty quantification metric, in *Reliability Engineering & System Safety*, 221, 108316,
(2022).
[12] H. Arslan, M. Aslan, & G.-W. Weber, Distance-based prediction intervals for time series
forecasting, arXiv preprint arXiv:2309.10613, (2023).
[13] G. Shmueli, To explain or to predict?, in *Statistical Science*, 25(3), 289–310, (2010).
[14] L. Wasserman, All of nonparametric statistics, New York, NY: Springer, (2006).
[15] M. Goldani & S. Asadi Tirvan, Sensitivity assessing to data volume for forecasting: introducing similarity methods as suitable ones in feature selection methods, in *Journal of
Mathematics and Modeling in Finance*, 4(2), 115–134, (2024).
[16] J. Y. L. Chan, S. M. H. Leow, K. T. Bea, W. K. Cheng, S. W. Phoong, Z. W. Hong,
& Y. L. Chen, Mitigating the multicollinearity problem and its machine learning approach:
a review, in *Mathematics*, 10(8), 1283, (2022).
[17] M. Arashi, M. Roozbeh, N. A. Hamzah, & M. Gasparini, Ridge regression and its applications in genetic studies, in *PLOS One*, 16(4), e0245376, (2021).
[18] S. Mermi, O. Akku, A. G ¨ okta, & N. G ¨ und ¨ uz¨ , A new robust ridge parameter estimator having no outlier and ensuring normality for linear regression model, in *Journal of Radiation
Research and Applied Sciences*, 17(1), 100788, (2024).
[19] E. Cule & M. De Iorio, Ridge regression in prediction problems: automatic choice of the
ridge parameter, in *Genetic Epidemiology*, 37(7), 704–714, (2013).
[20] H. Quan, D. Srinivasan & A. Khosravi, Short-Term Load and Wind Power Forecasting
Using Neural Network-Based Prediction Intervals, in *IEEE Transactions on Neural Networks
and Learning Systems*, 25(2), 303–315, (2014).
[21] A. M. Simundic, Confidence interval, in *Biochemia Medica*, 18(2), 154–161, (2008).
[22] A. Hazra, Using the confidence interval confidently, in *Journal of Thoracic Disease*, 9(10),
4125, (2017).