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Statistical analysis of subspace-based estimation of reduced-rank linear regressions

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2 Author(s)
Gustafsson, Tony ; Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA ; Rao, B.D.

A number of signal processing and system identification problems include linear regressions with a reduced-rank regression matrix. A typical step in "subspace-based" algorithms is to apply the singular value decomposition (SVD) to compute a low-rank factorization. However, it is not clear how certain weighting matrices should be defined for best possible accuracy. We present a statistical analysis of the estimate of the reduced-rank regression matrix, and we discuss a couple of approaches for finding weighting matrices

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Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 1 )