BibTeX
@MISC{Berger91andmaximum,
author = {Roger L. Berger and Roger Berger},
title = {AND MAXIMUM LIKELIHOOD ESTIMATES},
year = {1991}
}
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Abstract
Functions called generalized means are of interest in statistics because they are simple to compute, have intuitive appeal, and can serve as reasonable parameter estimates. arithmetic, geometric, and harmonic means are all examples of generalized means. The well-known We show how generalized means can be derived in a unified way, as least squares estimates for a transformed data set. We also investigate models that have generalized means as their maximum likelihood estimates.
