maximum likelihood estimator

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max·i·mum like·li·hood es·ti·ma·tor

the prescription "Assign to the unknown parameter that value that maximizes the likelihood for the sample." For many problems this procedure is an optimal one.
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Table 3 displays the maximum likelihood estimates of the parameters of the EOFNH, EOFW, OFNH, and OFW distributions with their corresponding standard errors in bracket and the model selection criteria.
In the second approach the maximum Likelihood estimate of parameters like PSF and covariance the PSF estimate is not unique other assumptions like size, symmetry etc.
Table 1: Maximum Likelihood Estimator's for Double Weibull Distribution and Weibull Distribution.
In this study, the maximum likelihood (ML) estimation to individual bond data is applied based on three representative structural models previously used to estimate bond yield spreads.
The wind speed data manageable in the Weibull's distribution is analyzed by using Modified Maximum Likelihood Estimation method (MMLE).
Keywords: SNR estimation, maximum likelihood, QAM, Rayleigh fading
Maximum Likelihood Estimation (MLE), as a nonlinear spectrum estimation method, was developed in the late 1960s due to the need of the Maximum Likelihood Principle of the seismic wave and the acoustic signal.
Iteratively applying local quadratic approximation to the likelihood (through the Fisher information [13]), the least squares method was used to fit a generalized linear model as a way of unifying classical, logistic, and Poisson (linear) regression in [14] by iteratively reweighing the least squares method in the way to the maximum likelihood estimation of the model parameters.
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