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Mean Likelihood Estimators
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| Organization: | University of Western Ontario |
| Department: | Department of Statistical and Actuarial Sciences |
| Organization: | Time Series Research and Analysis Centre, Statistics, Canada |
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The use of Mathematica in deriving mean likelihood estimators is discussed. Comparisons are made between the mean likelihood estimator, the maximum likelihod estimator, and the Bayes estimator based on a Jeffrey's noninformative prior. These estimators are compares using the mean-square error criterion and Pitman measure of closeness. In some cases it is possible, using Mathematica, to derive exact results for these criteria. Using Mathematica, simulation comparisons among the criteria can be made for any model for which we can readily obtain estimators.
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binomial distribution, exponential distribution, first-order moving-average time series model, Mathematica in education and research, mean square error criterion, Pitman measure of closeness, simulation comparison of estimators, unit root in MA(1) model
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