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We observe that the former is an optimum estimator, while the difference between (29) and CRLB is very small.
It can also be used to search for the optimum estimator parameters to give the minimum sum of variances.
It is well known that ML estimator is an optimum estimator since it can asymptotically achieve the Cramér-Rao lower bound (CRLB) for high signal-to-noise ratios (SNRs) and/or large signal bandwidths [9].
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Maximum likelihood estimation implemented by Newton-Raphson algorithm yielded optimum estimators for both the linear RIC and the nonlinear RC models.
Using a prediction technique similar to that employed under the model-based approach, we develop optimum estimators of the linear combinations of the unit parameters and optimum predictors of the random effects.
Several ratio and exponentiation type estimators that provide the optimum estimate of a quantile based on an optimum exponent α are proposed.
The optimum estimation method is usually the ML estimator which results in θ ̂ ML.
One of the earliest contributions for the phase estimation of binary phase shift keying (BPSK) signal is an optimum phase estimator which derives a reference signal from the received data itself using Costas loop [5].
Note that in order to derive the optimum STSA estimator in (2), it suffices to minimize the inner integral in (3) with respect to (hat {chi }).
It is shown that the adaptive windowed XWVD (AW-XWVD) is an optimum phase estimator as it meets the Cramer-Rao Lower Bound (CRLB) at signal-to-noise ratio (SNR) of 5 dB for both binary phase shift keying and quadrature phase shift keying.
That is, the performance of the LP estimator is optimum in the asymptotic sense.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com