Double shrinkage correction in sample LMMSE estimation
Document typeConference report
PublisherEuropean Association for Signal Processing (EURASIP)
Rights accessRestricted access - publisher's policy
The sample linear minimum mean square error (LMMSE) es- timator undergoes high performance degradation in the small sample size regime. Herein a double shrinkage correction is proposed to alleviate this problem. First, an af ne transfor- mation of the sample covariance matrix (SCM) is considered within the LMMSE. Second, a linear transformation of that modi ed lter is proposed. The linear transformation mini- mizes the asymptotic MSE of the lter given a shrinkage of the SCM. And the shrinkage of the SCM optimizes the as- ymptotic MSE of the data covariance. Simulations highlight that the proposed estimator outperforms robust methods to the small sample size, namely LMMSE based on diagonal load- ing (DL) or Ledoit-Wolf (LW) regularizations of the SCM
CitationSerra, J.; Najar, M. Double shrinkage correction in sample LMMSE estimation. A: European Signal Processing Conference. "Proceedings of the 21st European Signal Processing Conference (EUSIPCO), 2013: 9-13 September, 2013: Marrakech, Morocco". Marrakesh: European Association for Signal Processing (EURASIP), 2013, p. 1-5.