# E y e y x 102 x 10 e y 2 x 10 e y x

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Unformatted text preview: dentically distributed random variables, with mean µ and variance σ 2 . It might be that the mean and variance are unknown, and that the distribution is not even known to be a particular type, so maximum likelihood estimation is not appropriate. In this case it is reasonable to estimate µ and σ 2 by the sample mean and sample variance deﬁned as follows: X= 1 n n Xk k=1 σ2 = 1 n−1 n (Xk − X )2 . k=1 156 CHAPTER 4. JOINTLY DISTRIBUTED RANDOM VARIABLES Note the perhaps unexpected appearance of n − 1 in the sample variance. Of course, we should have n ≥ 2 to estimate the variance (assuming we don’t know the mean) so it is not surprising that the formula is not deﬁned if n = 1. An estimator is called unbiased if the mean of the estimator is equal to the parameter that is being estimated. (a) Is the sample mean an unbiased estimator of µ? (b) Find the mean square error, E [(µ − X )2 ], for estimation of the mean by the sample mean. (c) Is the sample variance an unbiased estimator of σ 2 ? Solution (a) By the linearity of expectation, E [...
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## This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Institute of Technology.

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