https://orcid.org/0000-0002-6630-0917
References
- Allam, A., and T. Mourid. 2019. Optimal rate for covariance operator estimators of functional autoregressive processes with random coefficients. Journal of Multivariate Analysis 169:130–7. doi:10.1016/j.jmva.2018.07.009.
- Amirmazlaghani, M., and H. Amindavar. 2007. Image denoising using two-dimensional GARCH model. Systems, Signals and Image Processing 14:397–400.
- Andel, J. 1976. Autoregressive series with random parameters. Mathematische Operations for Schung Und Statistik 7 (5):735–41.
- Aue, A., L. Horváth, and J. Steinebach. 2006. Estimation in random coefficient autoregressive models. Journal of Time Series Analysis 27 (1):61–76. doi:10.1111/j.1467-9892.2005.00453.x.
- Bibi, A. 2016. Two-stage generalized moment method approach for bidimensional random coefficient autoregressive models. Communications in Statistics-Theory and Methods 45 (14):4268–84. doi:10.1080/03610926.2014.919401.
- Bougerol, P., and N. Picard. 1992. Strict stationarity of generalized autoregressive processes. The Annals of Probability 20 (4):1714–30.
- Brandt, A. 1986. The stochastic equation Yn+1=AnYn+Bn with stationary coefficients. Advances in Applied Probability 18 (1):211–20.
- Goryainov, A. V., and E. R. Goryainova. 2016. Comparison of efficiency of estimates by the methods of least absolute deviations and least squares in the autoregression model with random coefficient. Automation and Remote Control 77 (9):1579–88. doi:10.1134/S000511791609006X.
- Goryainov, V. B., and E. R. Goryainova. 2019. Comparative analysis of robust and classical methods for estimating the parameters of a threshold autoregression equation. Automation and Remote Control 80 (4):666–75. doi:10.1134/S0005117919040052.
- Hamaz, A. 2019. Prediction of random fields with incomplete quarter-plane past. Communications in Statistics-Theory and Methods 48 (11):2707–16. doi:10.1080/03610926.2018.1472789.
- Hamaz, A., O. Arezki, and F. Achemine. 2020. Impact of missing data on the prediction of random fields. Journal of Applied Statistics 47 (1):132–49. doi:10.1080/02664763.2019.1633286.
- Leybourne, S. J., B. P. McCabe, and A. R. Tremayne. 1996. Can economic time series be differenced to stationarity. Journal of Business & Economic Statistics 14 (4):435–46.
- Mousazadeh, S., and I. Cohen. 2014. Two dimensional noncausal AR-ARCH model: Stationary conditions, parameter estimation and its application to anomaly detection. Signal Processing 98:322–36. doi:10.1016/j.sigpro.2013.12.003.
- Noiboar, A., and I. Cohen. 2007. Anomaly detection based on wavelet domain GARCH random field modeling. IEEE Transactions on Geoscience and Remote Sensing 45 (5):1361–73. doi:10.1109/TGRS.2007.893741.
- Regis, M., P. Serra, and E. R. van den Heuvel. 2022. Random autoregressive models: A structured overview. Econometric Reviews 41 (2):207–30. doi:10.1080/07474938.2021.1899504.
- Shitan, M., A. Thavaneswaran, and T. Vazifedan. 2015. First-order random coefficient autoregressive (RCA (1)) model: Joint Whittle estimation and information. Acta et Commentationes Universitatis Tartuensis de Mathematica 19 (1):3–10. doi:10.12697/ACUTM.2015.19.01.
- Tsay, R. S. 1987. Conditional heteroscedastic time series models. Journal of the American Statistical Association 82 (398):590–604. doi:10.1080/01621459.1987.10478471.