Outlier Resistant Time Series Operations via Qualitative Robustness and Saddle-Point Game Formalizations- A Review: Filtering and Smoothing
DOI:
https://doi.org/10.14738/tnc.61.2480Abstract
Abstract: Time series operations are sought in numerous applications, while the observations used in such operations are generally contaminated by data outliers. The objective is thus to design outlier resistant or “robust” time series operations whose performance is characterized by stability in the presence versus the absence of data outliers. Such a design is guided by the theory of qualitative robustness and is completed by saddle-point game formalizations. The approach is used for the development of outlier resistant filtering and smoothing operations.
References
(1) Hampel, F.R. (1971). A General Qualitative Definition of Robustnes. Ann. Math. Statist. 42, 1887-1895.
(2) Papantoni-Kazakos, P. and R.M. Gray (1979). Robustness of Estimators on Stationary Observations. Ann. Prob. 7, 989-1002.
(3) Cox, D (1978). Metrics on Stochastic Processes and Qualitative
Robustness. Tech. Report No. 23. Dept. of Staistics, Univ. of Wash., Seattle.
(4) Bustos, O., R. Fraiman and V. Yohai (1984). Asymptotic Behavior of the Estimates Based on Residual Autocovariances for ARMA Models. Robust and Nonlinear Time Series Analysis in Lecture Notes in Statistics. Springer-Verlag, New York, 26-49.
(5) Papantoni-Kazakos, P. (1984a). A Game Theoretic Approach to Robust Filtering. Information and Control 60, Nos. 1-3, 168-191.
(6) Papantoni-Kazakos, P. (1984b). Some Aspects of Qualitative Robustness in Time Series. Robust and Nonlinear Time Series Analysis in Lecture Notes in Statistics. Springer-Verlag, New York, 218-230.
(7) Papantoni-Kazakos, P. (1987). Qualitative Robustness in Time Series. Information and Computation 72, No. 3, 239-269.
(8) Tsaknakis, H. and P. Papantoni-Kazakos (1988). Outlier Resistant Filtering and Smoothing. Information and Computation 79, 163-192.
(9) Tsaknakis, H., D. Kazakos and P. Papantoni-Kazakos (1986). Robust Prediction and Interpolation for Vector Stationary Processes. Prob. Th. And Related Fields 72, Springer-Verlag, New York, 589-602.
(10) Kazakos, D. and P. Papantoni-Kazakos (1990). Detection and Estimation. Computer Science Press, New York.
(11) Papantoni-Kazakos, P. (1981a). Sliding Block Encoders that are Rho-
Bar Continuous Functions of Their Input. IEEE Trans. Inf. Theory, IT-27, 372-376.
(12) Papantoni-Kazakos, P. (1981b). Stochastic Quantization for Performance Stability. Information and Control 49, No. 3, 171-198.
(13) Kogiantis, A. and P. Papantoni-Kazakos (1997). Operations and Learning in Neural Networks for Robust Prediction. IEEE Trans. Systems, Man and Cybernetics 27, No. 3, 402-411.
(14) Burrell, A.T., A. Kogiantis and P. Papantoni-Kazakos (1997). Detecting Changes in Acting Stochastic Models and Model Implementation via Stochastic Neural Networks. Statistical Methods in Control and Signal Processing. Editors: S. Sugimoto and T. Katayama. Marcel Dekker.
(15) Burrell, A.T. and P. Papantoni-Kazakos (2012). Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping. International Journal of Communications Network and System Sciences (IJCNS). Special Issue on Models and Algorithms for Applications, September, Vol. 5, 603-608.
