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- W2100035501 abstract "A new biased linear estimator referred to as the covariance shaping least-squares (CSLS) estimator is proposed for estimating a set of unknown deterministic parameters in a linear model. The CSLS estimator is directed at improving the performance of the least-squares (LS) estimator by choosing the estimate to minimize the error variance in the observations subject to a constraint on its covariance. The CSLS estimator is shown to achieve the Cramer-Rao bound for biased estimators. Furthermore, the covariance of the estimate can be chosen such that there is a threshold SNR, below which the CSLS estimator yields a lower MSE than the LS estimator for all values of the parameters. Based on the concept of CSLS estimation, we propose a new linear receiver for multiuser detection in CDMA systems." @default.
- W2100035501 created "2016-06-24" @default.
- W2100035501 creator A5005913897 @default.
- W2100035501 date "2003-10-31" @default.
- W2100035501 modified "2023-09-26" @default.
- W2100035501 title "Covariance shaping approach to linear least-squares estimation" @default.
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- W2100035501 doi "https://doi.org/10.1109/acssc.2002.1197080" @default.
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