TY - GEN
T1 - On universal linear prediction of Gaussian data
AU - Kozat, Suleyman S.
AU - Singer, Andrew C.
N1 - Publisher Copyright:
© 2000 IEEE.
PY - 2000
Y1 - 2000
N2 - In this paper, we derive some of the stochastic properties of a universal linear predictor, through analyses similar to those generally made in the adaptive signal processing literature. A. C. Singer et al. (see IEEE Trans. Signal Proc., vol.47, no.10, p.2685-2700, Oct. 1999) introduced a predictor whose sequentially accumulated mean squared error for any bounded individual sequence was shown to be as small as that for any linear predictor of order less than some maximum order m. For stationary Gaussian time series, we generalize these results, and remove the boundedness restriction. In this paper we show that the learning curve of this universal linear predictor is dominated by the learning curve of the best order predictor used in the algorithm.
AB - In this paper, we derive some of the stochastic properties of a universal linear predictor, through analyses similar to those generally made in the adaptive signal processing literature. A. C. Singer et al. (see IEEE Trans. Signal Proc., vol.47, no.10, p.2685-2700, Oct. 1999) introduced a predictor whose sequentially accumulated mean squared error for any bounded individual sequence was shown to be as small as that for any linear predictor of order less than some maximum order m. For stationary Gaussian time series, we generalize these results, and remove the boundedness restriction. In this paper we show that the learning curve of this universal linear predictor is dominated by the learning curve of the best order predictor used in the algorithm.
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U2 - 10.1109/ICASSP.2000.861846
DO - 10.1109/ICASSP.2000.861846
M3 - Conference contribution
AN - SCOPUS:0033708397
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 13
EP - 16
BT - Signal Processing Theory and Methods I
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000
Y2 - 5 June 2000 through 9 June 2000
ER -