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Convergence rates of nonparametric filtering estimates in autoregression dynamic systems. (English. Russian original) Zbl 1117.62489
Autom. Remote Control 64, No. 1, 49-64 (2003); translation from Avtom. Telemekh. 2003, No. 1, 56-73 (2003).
Summary: The Bayes problem of filtration of a random process from observations on an autoregression process with coefficients defined by functions of the useful signal is studied. The main assumption asserts that the conditional observation densities belong to a family of conditional exponential densities with known functions of observations and useful signal, whose distribution is not known in advance. Optimal signal filtering equations and empirical risk estimates are derived. Regularized nonparametric filtering estimates are derived and a theorem on the mean-square convergence and convergence rates of these estimates is formulated.

62M20 Inference from stochastic processes and prediction
62G05 Nonparametric estimation
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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