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Unformatted text preview: MSE ( ) = E ( - ) 2 = V ar ( ) + bias ( 2 ) to compare the estimators. MSE ( b MOM ) = 1 n 1 MSE ( b MLE ) = 1 n 2 + b-1 n-b 2 = 2 n 2 MSE b MLE + 1 n = 1 n 2 Since MSE b MLE + 1 n MSE ( b MLE ) MSE ( b MOM ), b MLE + 1 n is the better of the three using this criterion. Also note that A MLE has fundamental aws. Depending on your sample points, the estimator may end up being larger than some of the observed values, which is impossible under the uniform distribution. 2...
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This note was uploaded on 07/22/2009 for the course IEOR 165 taught by Professor Shanthikumar during the Summer '08 term at University of California, Berkeley.
- Summer '08