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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Indeed the maximum likelihood estimators of [delta]'s do not take into account the loss in degrees of freedom that results from estimating [mu].
Bergstrom's expression for the exact density of the maximum likelihood estimator (in this case, indirect least squares or limited information maximum likelihood (LIML)) was easily found by transformation from the (normal) distribution of the reduced form coefficients and has the explicit form
The exact sampling distributions of least squares and maximum likelihood estimators of the marginal propensity to consume.
The maximum likelihood estimator of [Theta] is the value [Mathematical Expression Omitted
3 The principle of the weighted maximum likelihood estimator can be explained as follows: Suppose that the sample is intentionally drawn so that it consists of 50 percent insolvent insurers and 50 percent solvent insurers.
To test this hypothesis, the mean squared errors on the maximum likelihood estimators are compared with the empirical Bayes estimators.
This paper provides an Edgeworth expansion for the distribution of the maximum likelihood estimators (MLE) of the parameter of a time series generated by a linear regression model with Gaussian, stationary, long-memory errors.
Developed at Charles University in Prague, this graduate textbook explains the mathematical theory behind maximum likelihood estimators (M-estimators), L-estimators based on order statistics, R-estimators based on the ranks of their residuals, the multivariate location model, and some goodness-of-fit tests.
Fracdiff calculates the maximum likelihood estimators of the parameters of a fractionally-differenced ARIMA (p,d,q) model, together with their estimated covariance and correlation matrices and standard errors, as well as the value of the maximized likelihood.
Determination offish movement patterns from tag recoveries using maximum likelihood estimators.
In the logit model, however, there is no simple way to adjust maximum likelihood estimators for errors in variables, since the regression function is nonlinear.
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