You are not currently logged in.
Access your personal account or get JSTOR access 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.
Minimum Variance Estimation Without Regularity Assumptions
Douglas G. Chapman and Herbert Robbins
The Annals of Mathematical Statistics
Vol. 22, No. 4 (Dec., 1951), pp. 581-586
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2236927
Page Count: 6
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
Following the essential steps of the proof of the Cramér-Rao inequality [1, 2] but avoiding the need to transform coordinates or to differentiate under integral signs, a lower bound for the variance of estimators is obtained which is (a) free from regularity assumptions and (b) at least equal to and in some cases greater than that given by the Cramer-Rao inequality. The inequality of this paper might also be obtained from Barankin's general result . Only the simplest case--that of unbiased estimation of a single real parameter--is considered here but the same idea can be applied to more general problems of estimation.
The Annals of Mathematical Statistics © 1951 Institute of Mathematical Statistics