Find a consistent estimator of µ 2 , where E(Y ) = µ is the population mean and Y¯ n is the sample mean. 2 If E(Y 2 ) = µ 0 2 then prove that 1 n Pn i=1 Y 2 i is an consistent estimator of µ 0 2 3 We define σ 2 = µ 0 2 − µ 2 . Show that S 2 n = 1 n Pn i=1 Y 2 i − Y¯ 2 n is a consistent estimator of σ 2 . 4 Show that hence, S 2 is a consistent estimator of σ 2 .
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Find a consistent estimator of µ 2 , where E(Y ) = µ is the population mean and Y¯ n is the sample mean. 2 If E(Y 2 ) =...
To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with the finite variance σ 2 , we first take a random sample of size n . Then, we randomly draw one of n slips of paper numbered from 1 through n , and • if the number we draw is 2, 3, ··· , or n , we use as our estimator the...
1. Let Yı,Y2,..., Yn denote a random sample from a population with mean E (-0,) and variance o2 € (0,0). Let Yn = n- Y. Recall that, by the law of large numbers, Yn is a consistent estimator of . (a) (10 points) Prove that Un="in is a consistent estimator of . (b) (5 points) Prove that Vn = Yn-n is not a consistent estimator of (c) (5 points) Suppose that, for each i, P(Y, - of ? Prove what...
To show that an estimator can be consistent without being unbiased or even asymptotically the finite variance σ, we first take a random sample of size n. Then we randomly draw one of n slips of paper numbered from 1 through n, and if the number we draw is 2, 3,.., or n, we use as our estimator the mean of the random sample; otherwise, we use the estimate n2. Show that this estimation procedure is (a) consistent; (b) neither...
To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with the nite variance 2, we rst take a random sample of size n. Then, we randomly draw one of n slips of paper numbered from 1 through n, and if the number we draw is 2, 3, , or n, we use as our estimator the mean of the random sample; otherwise, we...
To show that an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with randomly draw o slips of paper numbered from 1 through n, and if the number we draw is 2, 3,.. .or n, we use as our estimator the mean of the random sample; otherwise, we use the estimate n2. Show that this estimation procedure is (a) consistent; (b) neither unbiased nor asymptotically...
Please answer as neatly as possible. Much thanks in advance! Question 1: 6. In Problem 1, show that θ2 is a consistent estimator for θ. Deduce that Y(n) is a consistent estimator for θ and also asyınpt○tically unbiased estimator for θ. 1. Let Yi, ½, . . . ,y, denote a random sample from an uniform distribution on the interval (0,0). We have seen that (1) and 62 Ym are unbiased estimators for 0. Find the efficiency of 6 relative...
10.41] To show an estimator can be consistent without being unbiased or even asymptotically unbiased, consider the following estimation procedure: To estimate the mean of a population with the finite variance σ2, we first take a random sample of size n. Then, we randomly draw one of n slips of paper numbered from 1 through n, and if the number we draw is 2, 3, ..., orn, we use as our estimator the mean of the random sample; otherwise, we...
(3) Unbiased Estimator Y is distributed N( 14, a2 ). Weighted sample mean is defined in the following: N Σ4r Y = - S.Σα =Ν (a) Please prove weighted sample mean is unbiased (3) Unbiased Estimator Y is distributed N( 14, a2 ). Weighted sample mean is defined in the following: N Σ4r Y = - S.Σα =Ν (a) Please prove weighted sample mean is unbiased
QUESTION 2 Let Xi.. Xn be a random sample from a N (μ, σ 2) distribution, and let S2 and Š-n--S2 be two estimators of σ2. Given: E (S2) σ 2 and V (S2) - ya-X)2 n-l -σ (a) Determine: E S2): (l) V (S2); and (il) MSE (S) (b) Which of s2 and S2 has a larger mean square error? (c) Suppose thatnis an estimator of e based on a random sample of size n. Another equivalent definition of...
2. (a) Define the bias of ˆ θ as an estimator for the parameter θ. [2 marks] (b) For independent random variables X1,X2,...,Xn, assume that E(Xi) = µ and var(Xi) = σ2, i = 1,...,n. (i) Show that ˆ µ1 = {(X1+Xn)/2}is an unbiased estimator for µ and determine its variance. [3 marks] (ii) Find the relative efficiency of ˆ µ1 to the unbiased estimator ˆ µ2 = X, the sample mean. [2 marks] (iii) Is ˆ µ1 a consistent...