[1] Abdollahzadeh, A. Baagherzadeh Hushmandi, and P. Nabati, Improving the accuracy of financial time series prediction using nonlinear exponential autoregressive models. Journal of Mathematics and Modeling in Finance, 4(1) (2024), 159--173.
[2]E. Alpaydin, Introduction to Machine Learning, the MIT Press, 2010.
[3]M. Viana, K. Oliveira, Foundations of ergodic theory, No. 151, Cambridge University Press, 2016.
[3]Y. Hmamouche, P. Przymus, A. Casali, and L. Lakhal, GFSM: a feature selection method for improving time series forecasting, Int. J. Adv. Syst. Meas, (2017).
[4] Z. Cui and G. Gong, The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features, Neuroimage, 178 (2018), 622--637.
[5] E. Scornet, G. Biau, and J. P. Vert, Consistency of random forests, The Annals of Statistics, 43(4) (2015), 1716--1741.
[6] B. Bader and J. Yan, eva: R package for extreme value analysis with goodness-of-fit testing, R package version 0.2.6, 2020.
[7] K. Haddad, A. Rahman, and J. Green, Design rainfall estimation in Australia: A case study using L-moments and generalized least squares regression, Stochastic Environmental Research and Risk Assessment, 25 (2011), 815--825.
[8] T. R. Kjeldsen and D. A. Jones, Sampling variance of flood quantiles from the generalized logistic distribution estimated using the method of L-moments, Hydrology and Earth System Sciences, 8 (2004), 183--190.
[9] K. Firouzi and M. J. Mamaghani, Log-ergodicity: A New Concept for Modeling Financial Markets, Statistics Optimization and Information Computing, IAPress, 2024.
[10] P. C. Austin, D. S. Lee, and J. P. Fine, Introduction to the analysis of survival data in the presence of competing risks, Circulation, 133 (2016), 601--609.
[11] J. L. Moscovici and B. Ratitch, Combining Survival Analysis Results after Multiple Imputation of Censored Event Times, In Proceedings of PharmaSUG, 2017.
[12] P. McCullagh and J. A. Nelder, Generalized Linear Models, Chapman and Hall, 1989.
[13] P. Dunn and G. Smyth, Generalized Linear Models with Examples in R, Springer, 2018.
[14] S. A. Klugman, H. H. Panjer, and G. E. Willmot, Loss Models: From Data to Decisions, Wiley Series in Probability and Statistics, 2019.
[15] R. H. Myers, D. C. Montgomery, and G. G. Vining, Generalized Linear Models with Applications in Engineering and the Sciences, Wiley, 2012.
[16] J. P. Klein and M. L. Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data, Springer, 2003.
[17] B. J. Gajewski, N. Nannette, and J. E. Widen, Predicting Hearing Threshold in Nonresponsive Subjects Using a Log-Normal Bayesian Linear Model in the Presence of Left-Censored Covariates, 2012.
[18] J. F. Lawless, Statistical Models and Methods for Lifetime Data, Wiley, 2003.
[19] J. F. Dupuy, Censored Gamma Regression with Uncertain Censoring Status, Mathematical Methods of Statistics, 29(4) (2022).
[20] B. Wang, C. Li, P. Liu, A. Latengbaolide, and L. Yang, Log-normal censored regression model detecting prognostic factors in gastric cancer: A study of 3018 cases, 2011.
[21] S. G. Mingari, D. Ritelli, and D. Spelta, Actuarial values calculated using the incomplete Gamma function, Statistica, 66(1) (2006), 77--84.
[22] M. Z. Rehman, et al., Choice between sustainable versus conventional investments: Relative efficiency analysis, Sustainability, 2024.
[23] V. T. Nguyen and J. F. Dupuy, Asymptotic results in censored zero-inflated regression model, Communications in Statistics - Theory and Methods, 2021.
[24] P. J. Bickel, C. A. Klaassen, Y. Ritov, and J. A. Wellner, Efficient and adaptive estimation for semiparametric models, Springer, 1993.
[25] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control, John Wiley & Sons, 2015.
[26] T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 785--794.
[27] N. A. Cressie, Statistics for spatial data, John Wiley & Sons, 1993.
[28] B. Efron and R. J. Tibshirani, An introduction to the bootstrap, CRC press, 1994.
[29] J. Fan, R. Li, and C. H. Zhang, Statistical foundations of data science, CRC Press, 2020.
[30] C. Francq and J. M. Zakoïan, GARCH models: structure, statistical inference and financial applications, John Wiley & Sons, 2019.
[31] A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian data analysis, CRC press, 2013.
[32] A. C. Harvey, Forecasting, structural time series models and the Kalman filter, Cambridge university press, 1990.
[33] T. Hastie and R. Tibshirani, Varying-coefficient models, Journal of the Royal Statistical Society: Series B, 55(4) (1993), 757--779.
[34] T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning: data mining, inference, and prediction, Springer, 2009.
[35] D. Kwiatkowski, P. C. Phillips, P. Schmidt, and Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root, Journal of econometrics, 54(1-3) (1992), 159--178.
[36] A. J. McNeil, R. Frey, and P. Embrechts, Quantitative risk management: concepts, techniques and tools, Princeton university press, 2015.
[37] W. K. Newey, The asymptotic variance of semiparametric estimators, Econometrica, 62(6) (1994), 1349--1382.
[39] P. C. Phillips and P. Perron, Testing for a unit root in time series regression, Biometrika, 75(2) (1988), 335--346.
[40] C. E. Rasmussen and C. K. Williams, Gaussian processes for machine learning, MIT press, 2006.
[41] D. Ruppert, M. P. Wand, and R. J. Carroll, Semiparametric regression, Cambridge University Press, 2003.
[42] R. S. Tsay, Analysis of financial time series, John Wiley & Sons, 2010.
[43] J. M. Wooldridge, Econometric analysis of cross section and panel data, MIT press, 2010.
)