Rank reduction principle trades model bias for variance in stationary time series modeling and analysis, highlighting insights.
Rank reduction is developed as a general principle for trading off model bias and model variance in the analysis and synthesis of signals. The principle is applied to three basic problems: stationary time series modeling, stationary time series whitening, and vector quantization. Each problem brings its own surprises and insights.
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Scharf et al. (1987) studied this question.
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