Time series AR modeling with missing observations based on the polynomial transformation. (English) Zbl 1190.62157

Ding, Jie; Han, Lili; Chen, Xiaoming

Math. Comput. Modelling 51, No. 5-6, 527-536 (2010).

Summary: This paper focuses on parameter estimation problems of auto-regressive (AR) time series models with missing observations. The standard estimation algorithms cannot be applied to such AR models with missing observations. A polynomial transformation technique is employed to transform the AR models into models which can be identified from available scarce observations and then the extended stochastic gradient algorithm is proposed to fit the time series with missing observations. The convergence properties of the proposed algorithm are analyzed and an example is given to test and illustrate the conclusions.

MSC:

62M10	Time series, auto-correlation, regression, etc. in statistics (GARCH)
62F10	Point estimation
62F12	Asymptotic properties of parametric estimators
65C60	Computational problems in statistics (MSC2010)

Keywords:

convergence properties

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