Title :
Statistical analysis of subspace-based estimation of reduced-rank linear regressions
Author :
Gustafsson, Tony ; Rao, Bhaskar D.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
fDate :
1/1/2002 12:00:00 AM
Abstract :
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
Keywords :
identification; parameter estimation; signal processing; singular value decomposition; statistical analysis; SVD; low-rank factorization; reduced-rank linear regressions; reduced-rank regression matrix; signal processing; singular value decomposition; statistical analysis; subspace-based algorithms; subspace-based estimation; system identification; weighting matrices; Eigenvalues and eigenfunctions; Linear regression; Matrix decomposition; Noise measurement; Random processes; Signal processing algorithms; Singular value decomposition; State-space methods; Statistical analysis; System identification;
Journal_Title :
Signal Processing, IEEE Transactions on
