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2. Let X1, X2, ...,Xn be a random sample from N(0,0), with 0 > 0 unknown. We used e instead of the usual o2 to make things easier to follow. a. Obtain a one-dimensional complete sufficient statistic for 0. b. Obtain the unbiased estimator of that is a function only of the CSS in (a). In other words, find an unbiased estimator based on the CSS in (a). c. Obtain the Cramer-Rao Lower Bound for the variance of any unbiased estimator of e. d. Get the variance of your unbiased estimator in (b) and show that it is an efficient estimator of e. e. Use the Information Inequality Theorem to show that your estimator in (b) is the UMVUE of 0. (In other words, show that there exists a function K(0, n) to show the equality specified in the theorem) f. Obtain the MLE of vo. g. Obtain the asymptotic distribution of your MLE in (f).