Autoregressive representations of multivariate stationary stochastic processes
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Publication:1099877
DOI10.1007/BF00356109zbMath0639.60045MaRDI QIDQ1099877
Publication date: 1988
Published in: Probability Theory and Related Fields (Search for Journal in Brave)
autoregressive representationAbel summableCesáro summablelinear least squares predictorweakly stationary stochastic process
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Stationary stochastic processes (60G10) General second-order stochastic processes (60G12) Prediction theory (aspects of stochastic processes) (60G25)
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Cites Work
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- The prediction theory of multivariate stochastic processes. I. The regularity condition. - II. The linear predictor
- The prediction theory of multivariate stochastic processes. III: Unbounded spectral densities
- A matricial extension of the Helson-Szegö theorem and its application in multivariate prediction
- On the angle between past and future for multivariate stationary stochastic processes
- Shift invariant spaces and prediction theory
- On series representations for linear predictors
- A problem in prediction theory
- Inclusion relations among methods of summability compounded from given matrix methods
- The Helson-Sarason-Szego Theorem and the Abel Summability of the Series for the Predictor
- Systems of Toeplitz Operators on H 2 . II
- Summability of Fourier Series in L p (dμ)
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