TY - GEN
T1 - Maximum likelihood estimation in random linear models
T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
AU - Wiesel, Ami
AU - Eldar, Yonina C.
PY - 2006
Y1 - 2006
N2 - We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a Gaussian model matrix. The matrix has a known mean and independent rows of equal covariance matrix. Our problem formulation also allows for some known columns within this model matrix. We derive the maximum likelihood (ML) estimator associated with this problem and show that it can be found using a simple line-search over a unimodal function which can be efficiently evaluated. We then analyze its asymptotic performance using the Cramer Rao bound. Finally, we discuss the similarity between the ML, total least squares (TLS), and regularized TLS estimators.
AB - We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a Gaussian model matrix. The matrix has a known mean and independent rows of equal covariance matrix. Our problem formulation also allows for some known columns within this model matrix. We derive the maximum likelihood (ML) estimator associated with this problem and show that it can be found using a simple line-search over a unimodal function which can be efficiently evaluated. We then analyze its asymptotic performance using the Cramer Rao bound. Finally, we discuss the similarity between the ML, total least squares (TLS), and regularized TLS estimators.
UR - http://www.scopus.com/inward/record.url?scp=33947622149&partnerID=8YFLogxK
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AN - SCOPUS:33947622149
SN - 142440469X
SN - 9781424404698
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - V993-V996
BT - 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Y2 - 14 May 2006 through 19 May 2006
ER -