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Low Rank Estimation of Higher Order Statistics

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Title: Low Rank Estimation of Higher Order Statistics
Author: Nowak, Robert David; Van Veen, Barry D.
Type: Journal Paper
Keywords: Temporary
Citation: R. D. Nowak and B. D. Van Veen, "Low Rank Estimation of Higher Order Statistics," IEEE Transactions on Signal Processing, 1995.
Abstract: Low rank estimators for higher order statistics are considered in this paper. Rank reduction methods offer a general principle for trading estimator bias for reduced estimator variance. The bias-variance tradeoff is analyzed for low rank estimators of higher order statistics using a tensor product formulation for the moments and cumulants. In general the low rank estimators have a larger bias and smaller variance than the corresponding full rank estimator. Often a tremendous reduction in variance is obtained in exchange for a slight increase in bias. This makes the low rank estimators extremely useful for signal processing algorithms based on sample estimates of the higher order statistics. The low rank estimators also offer considerable reductions in the computational complexity of such algorithms. The design of subspaces to optimize the tradeoffs between bias, variance, and computation is discussed and a noisy input, noisy output system identification problem is used to illustrate the results.
Date Published: 1995-12-01

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  • ECE Publications [1030 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.