Please solve these questions 1. Suppose that X1, X2, and Xs are random variables with common...
. If X1, X2,..., Xn are independent random variables with common mean μ and variances σ1, σ2, . . ., σα , prove that Σί (Xi-T)2/[n(n-1)] is an ว. 102n unbiased estimate of var[X] 3. Suppose that in Exercise 2 the variances are known. LeTw Σί uiXi
ULLL Dsu i8 an unbiased estimate of Umin 4. The random variables X1, X2, . . . .xn have a common nonzero mean μ, a common variance σ2, and the correlation between any pair of random variables is ρ. (a) Find var (b) If and hence prove that-1/(n-1) バ1. 71 仁1 is an unbiased estimate of σ2, find a and b. Hence show that, i this case,
please answer the questions easily Suppose X1, X2, X3 is a random sample from a normal population with mean μ and variance (a) I,'ind i.he variallex, of Y , x..:.: Xy/X.t as an ( tinai." r of μ (b) Find the variance of Z-A+x2+x3 as an estimator of μ. (c) Which estimator is more efficient (i.e. has the smallest variance)? Consider a random sample of size n from a normal population with known mean μ and unknown variance σ2. Let...
Suppose that X1, X2 are two independent random variables with a common mean μ, but two different variances σ12 > σ22. Consider the family of estimators Wα = αX1 + (1−α)X2 where 0 ≤ α ≤ 1 (a) Show that Wα is an unbiased estimator of μ for any value of α. (b) Find the value of α which makes Wα as efficient as possible. Explain why the resulting formula makes sense.
Suppose that X1, X2,.... Xn and Y1, Y2,.... Yn are independent random samples from populations with the same mean μ and variances σ., and σ2, respectively. That is, x, ~N(μ, σ ) y, ~ N(μ, σ ) 2X + 3Y Show that is a consistent estimator of μ.
Let X1 and X2 be independent random variables with mean μ and variance σ2. Suppose we have two estimators 1 (1) Are both estimators unbiased estimatros for θ? (2) Which is a better estimator?
3. Let X1, X2, . . . , Xn be random variables with a common mean μ. Sup- pose that cov[Xi, xj] = 0 for all i and A such that j > i+1. If 仁1 and 6 VECTORS OF RANDOM VARIABLES prove that = var X n(n- 3)
X1, X2, . . . , Xn i.i.d. ∼ N (µ, σ2 ). Assume µ is known; show that ˆθ = 1 n Pn i=1(Xi− µ) 2 is the MLE for σ 2 and show that it is unbiased. Exactly 6.4-2. Xi, X2, . . . , xn i d. N(μ, μ)2 is the MLE for σ2 and show that it is unbiased. r'). Assume μ is known; show that θ- n Ση! (X,-
Problem 7. Let Xi, X2,..., Xn be i.i.d. (independent and identically distributed) random variables with unknown mean μ and variance σ2. In order to estimate μ and σ from the data we consider the follwing estimates n 1 Show that both these estimates are unbiased. That is, show that E(A)--μ and
Let X1,X2, , Xn be a random sample from a normal distribution with a known mean μ (xi-A)2 and variance σ unknown. Let ơ-- Show that a (1-α) 100% confidence interval for σ2 is (nơ2/X2/2,n, nơ2A-a/2,n). Let X1,X2, , Xn be a random sample from a normal distribution with a known mean μ (xi-A)2 and variance σ unknown. Let ơ-- Show that a (1-α) 100% confidence interval for σ2 is (nơ2/X2/2,n, nơ2A-a/2,n).