Penalized maximum-likelihood estimation of covariance matrices with linear structure

@article{Schulz1997PenalizedME,
  title={Penalized maximum-likelihood estimation of covariance matrices with linear structure},
  author={Timothy J. Schulz},
  journal={IEEE Trans. Signal Processing},
  year={1997},
  volume={45},
  pages={3027-3038}
}
y In this paper, a space-alternating generalized expectation-maximization (SAGE) algorithm is presented for the numerical computation of maximum-likelihood (ML) and penalized maximum-likelihood (PML) estimates of the parameters of covariance matrices with linear structure for complex Gaussian processes. By using a less informative hidden-data space and a sequential parameter-update scheme, a SAGE-based algorithm is derived for which convergence of the likelihood is demonstrated to be… CONTINUE READING
7 Citations
18 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 18 references

Fundamentals of Statistical Signal Processing: Estimation Theory

  • S. M. Kay
  • Prentice Hall, Englewood Cli s, New Jersey
  • 1993

Similar Papers

Loading similar papers…