Volume 4 • Issue 2 • PP: 06–11 • 2024
A Novel Comparison between the Ordinary Estimators and Robust Estimators for the Parameters of Some Binary Mixed Models
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© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Abstract
A condensed study will be done to compare the ordinary estimators. In particular, the maximum likelihood estimator and the robust estimator, to estimate the parameters of the mixed model of order one, namely BARMA (1, 1). Simulation experiments will be applied for varieties of BARMA (1, 1) based on using small, moderate, and large sample sizes, where some new results were obtained. MAPE was used as a statistical criterion for comparison.
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References
[1] S. Y. Safawi, “Comparison of normal and robust estima-tors of mixed binary time series models of lower ranks,” Ph.D. dissertation, Faculty of Computer Science and Mathematics, University of Mosul, 2005.
[2] M. D. Abdul-Ahad, “Hippocampal estimation in a first-order self-regression model,” unpublished master’s the-sis, Faculty of Computer Science and Mathematics, Uni-versity of Mosul, 2004.
[3] R. D. Martin, “Robust estimation of autoregressive mod-els,” in Directions in Time Series, D. R. Brillinger and G. C. Tiao, Eds. Hayward, CA, USA: Institute of Math-ematical Statistics, 1980.
[4] A. J. Fox, “Outliers in time series,” Journal of the Royal Statistical Society: Series B, vol. 34, pp. 350–363, 1973.
[5] N. Stochinger and R. Duter, “Robust time series analy-sis: A survey,” supplement to Kybernetika, 1987.
[6] J. E. Zch, “Efficiency robustness of generalized M-estimates for autoregression and their use in determining outliers,” Ph.D. dissertation, University of Washington, Seattle, WA, USA, 1979.
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