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Mean Likelihood Estimators

A. Ian McLeod
Organization: University of Western Ontario
Department: Department of Statistical and Actuarial Sciences
URL: http://www.stats.uwo.ca/faculty/aim/
B. Quenneville
Organization: Time Series Research and Analysis Centre, Statistics, Canada
Journal / Anthology

Statistics and Computing
Year: 2001
Volume: 11
Page range: 57-65

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.

*Mathematics > Probability and Statistics

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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