Rank reduction for modeling stationary signals

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.