You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Sets of Estimates of Location
Edward E. Leamer
Vol. 49, No. 1 (Jan., 1981), pp. 193-204
Published by: The Econometric Society
Stable URL: http://www.jstor.org/stable/1911133
Page Count: 12
You can always find the topics here!Topics: Mathematical theorems, Convexity, Covariance, Mathematical functions, Maximum likelihood estimators, Mathematical minima, Statistics, Mathematical problems, Matrices, Maximum likelihood estimation
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
If independent observations x are drawn from the distribution located at @m, f (x; @m)=c"3 exp[-g(x -@m)], and if g is symmetric and strictly convex, then the maximum likelihood estimate of μ lies between the smallest and largest folded sample observations. If the distribution has fatter tails than a normal distribution, then the maximum likelihood estimate lies between the smallest and largest means of trimmed subsamples. If the distribution is assumed to be symmetric and unimodal, the centers of tight clusters of observations can be maximum likelihood estimates. If observations are not independent, then there is no bound: given any example any number is a maximum likelihood estimate for some sampling distribution. Stationary is not sufficient to bound the estimate between the minimum and maximum observations.
Econometrica © 1981 The Econometric Society