1. ذبیحی مقدم ر.، یارمحمدی م.، حسنی ح. و نصیری پ. (۱۴۰۱)، بررسی بهبود پیش بینی بازگشتی روش تحلیل مجموعه مقادیر تکین در مدل های سری زمانی ساختاری با استفاده از پالایش داده ها و الگوریتم وزنی، مجله علوم آماری، 16(2)، 373- 395. 2. Bernhardt, C., Klüppelberg, C. and Meyer-Brandis, T. (2008), Estimating high quantiles for electricity prices by stable linear models, Journal of Energy Markets, 1(1), 3-19. [ DOI:10.21314/JEM.2008.002] 3. Bosq, D. (1991). Modelization, nonparametric estimation and prediction for continuous time processes. Nonparametric functional estimation and related topics, 509-529. Springer. [ DOI:10.1007/978-94-011-3222-0_38] 4. Chen, Z., Bao, Y., Li, H. and Spencer Jr, B. F. (2019), LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data. Mechanical Systems and Signal Processing, 121, 655-674. [ DOI:10.1016/j.ymssp.2018.11.052] 5. Condino, F. (2023), Share density-based clustering of income data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 16(4), 336-347, Wiley Online Library. [ DOI:10.1002/sam.11619] 6. Golyandina, N., Nekrutkin, V. and Zhigljavsky, A. A. (2001), Analysis of time series structure: SSA and related techniques. Chapman and Hall/CRC, New York, NY USA. [ DOI:10.1201/9781420035841] 7. Golyandina, N. and Osipov, E. (2007), The "Caterpillar"-SSA method for analysis of time series with missing values. Journal of Statistical Planning and Inference, 137(8), 2642-2653, Elsevier. [ DOI:10.1016/j.jspi.2006.05.014] 8. Haghbin, H., Morteza Najibi, S., Mahmoudvand, R., Trinka, J. and Maadooliat, M. (2021), Functional Singular Spectrum Analysis, Stat, 10(1), e330. [ DOI:10.1002/sta4.330] 9. Haghbin, H. and Maadooliat, M. (2024). A journey from univariate to multivariate functional time series: A comprehensive review. Wiley Interdisciplinary Reviews: Computational Statistics, 16(1), e1640. [ DOI:10.1002/wics.1640] 10. Hyndman, R. and Ullah, S. (2007). Robust forecasting of mortality and fertility rates: A functional data approach, Computational Statistics & Data Analysis, 51(10), 4942-4956. [ DOI:10.1016/j.csda.2006.07.028] 11. Hyndman, R. and Shang, H. L. (2009), Functional time series forecasting. Journal of the Korean Statistical Society, 38(3), 199-211. [ DOI:10.1016/j.jkss.2009.06.002] 12. Hörmann, S. and Kokoszka, P. P. (2012), Functional time series. In Time Series Analysis, Handbook of Statistics (Vol. 30, pp. 157-186), Elsevier B.V., Amsterdam. [ DOI:10.1016/B978-0-444-53858-1.00007-7] 13. Horváth, L. and Kokoszka, P. (2012), Inference for functional data with applications. Springer Science & Business Media. [ DOI:10.1007/978-1-4614-3655-3] 14. Kneip, A. and Utikal, K. J. (2001), Inference for density families using functional principal component analysis. Journal of the American Statistical Association, 96(454), 519-542. [ DOI:10.1198/016214501753168235] 15. Kokoszka, P., Miao, H., Petersen, A. and Shang, H. L. (2019), Forecasting of density functions with an application to cross-sectional and intraday returns. International Journal of Forecasting, 35(4), 1304-1317. [ DOI:10.1016/j.ijforecast.2019.05.007] 16. Pascariu, M. D., Lenart, A. and Canudas-Romo, V. (2019), The maximum entropy mortality model: Forecasting mortality using statistical moments. Scandinavian Actuarial Journal, 2019(8), 661-685. [ DOI:10.1080/03461238.2019.1596974] 17. Petersen, A. and Müller, H.-G. (2016), Functional data analysis for density functions by transformation to a Hilbert space. The Annals of Statistics, 44, 183-218. [ DOI:10.1214/15-AOS1363] 18. Ramsay, J. O. and Silverman, B. W. (2005), Functional data analysis. Springer-Verlag, New York, NY USA. [ DOI:10.1007/b98888] 19. Ramsay, J. O. (1998), Estimating smooth monotone functions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(2), 365-375. [ DOI:10.1111/1467-9868.00130] 20. Ramsay, J. O. (2000), Differential equation models for statistical functions. Canadian Journal of Statistics, 28(2), 225-240. [ DOI:10.2307/3315975] 21. Rodrigues, P. C. and Mahmoudvand, R. (2016), Correlation analysis in contaminated data by singular spectrum analysis. Quality and Reliability Engineering International, 32(6), 2127-2137. [ DOI:10.1002/qre.2027] 22. Shang, H. L., Haberman, S. and Xu, R. (2022), Multi-population modelling and forecasting life-table death counts. Insurance: Mathematics and Economics, 106, 239-253. [ DOI:10.1016/j.insmatheco.2022.07.002] 23. Trinka, J., Haghbin, H. and Maadooliat, M. (2022), Multivariate functional singular spectrum analysis: A nonparametric approach for analyzing multivariate functional time series. In Innovations in multivariate statistical modeling: Navigating theoretical and multidisciplinary domains (pp. 187-221). Springer. [ DOI:10.1007/978-3-031-13971-0_9] 24. Trinka, J., Haghbin, H., Lin Shang, H. and Maadooliat, M. (2023), Functional time series forecasting: Functional singular spectrum analysis approaches, Stat, 12(1), e621. [ DOI:10.1002/sta4.621] 25. Zabihi Moghadam R, Yarmohammadi M, Hassani H and Nasiri P. (2023), Investigating the Improvement of Recurrent Forecasting of Singular Spectrum Analysis Method in Structural Time Series Models Using Data Filtration and Weighting Algorithm. Journal of Statistical Sciences, 16 (2), 373-395. [ DOI:10.52547/jss.16.2.373]
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