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Unformatted text preview: Mean Squared Error and Maximum Likelihood: Lecture XVIII Charles B. Moss October 12, 2010 I. Mean Squared Error A. As stated in our discussion on closeness, one partential measure for the goodness of an estimator is E 2 (1) B. In the preceding example, the mean square error of the estimate can be written as E h ( T ) 2 i (2) where is the true parameter value between zero and one. C. This expected value is conditioned on the probability of T at each level value of . For example, if = 0 then the probability of each X becomes P [ X, ] = X (1 ) 1 X (3) If the two events are independent P [ X 1 , X 2 , ] = X 1 + X 2 (1 ) 1 X 1 X 2 (4) The mean squared error at any theta can then be derived as MSE ( ) = P [0 , , ] (0 ) 2 +2 P [0 , 1 , ](0 . 5 ) 2 + P [1 , 1 , ] (1 ) 2 . (5) 1 AEB 6571 Econometric Methods I Professor Charles B. Moss Lecture XVIII Fall 2010 Figure 1: Comparison of MSE for Various Estimators D. The mean squared error for S can similarly be computed as MSE ( ) = P [0 , ] (0 ) 2 + P [1 , ] (1 ) 2 (6) E. Finally, the mean square error of W can be written as MSE ( ) = (0 . 5 ) 2 (7) F. The mean squared error for each estimator is presented in Figure 1....
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This note was uploaded on 07/15/2011 for the course AEB 6180 taught by Professor Staff during the Spring '10 term at University of Florida.
- Spring '10