X1,...,Xn are IID with N(0,2).
a) Determine the mean and variance for (X (subscript 1)^2)
b) Show
sqrt(n) * [ log ( 1/n ∑(from i=1 to n) Xi2) − log(σ2 ) ] d → N(0, 2).
X1,...,Xn are IID with N(0,2). a) Determine the mean and variance for (X (subscript 1)^2) b)...
Assume X1, . . . , Xn iid normal with mean and variance ^2 , show that a. X¯ and X^2 are independent. b. Proof that X¯ is normally distributed with mean and variance ^2/n. c. Proof that (n ? 1)S^2/?2 is chi-squared distributed with (n ? 1) degrees of freedom. d. Show that X¯ S/is t distributed with (n ? 1) degrees of freedom
Let X1, X2,.......Xn be a random sample of size n from a continuous distribution symmetric about . For testing H0: = 10 vs H1: < 10, consider the statistic T- = Ri+ (1-i), where i =1 if Xi>10 , 0 otherwise; and Ri+ is the rank of (Xi - 10) among |X1 -10|, |X2-10|......|Xn -10|. 1. Find the null mean and variance of T- . 2. Find the exact null distribution of T- for n=5. We were unable to transcribe this imageWe were...
let x1.........xn be independent where xi is normally distributed with unknown mean u and unknown variance 0 find the UMP test for testing =0 against 0 when it is assumed that is known.=1 We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Suppose X1, X2, . . . , Xn are i.i.d. Exp(µ) with the density f(x) = for x>0 (a) Use method of moments to find estimators for µ and µ^2 . (b) What is the log likelihood as a function of µ after observing X1 = x1, . . . , Xn = xn? (c) Find the MLEs for µ and µ^2 . Are they the same as those you find in part (a)? (d) According to the Central Limit...
Let X1,X2,...,Xn denote independent and identically distributed random variables with mean µ and variance 2. State whether each of the following statements are true or false, fully justifying your answer. (a) T =(n/n-1)X is a consistent estimator of µ. (b) T = is a consistent estimator of µ (assuming n7). (c) T = is an unbiased estimator of µ. (d) T = X1X2 is an unbiased estimator of µ^2. We were unable to transcribe this imageWe were unable to transcribe...
Let X = (X1, . . . , Xn) be a random sample of size n with mean μ and variance σ2. Consider Tm i=1 (a) Find the bias of μη(X) for μ. Also find the bias of S2 and ỡXX) for σ2. (b) Show that Hm(X) is consistent. (c) Suppose EIXI < oo. Show that S2 and ỡXX) are consistent. Let X = (X1, . . . , Xn) be a random sample of size n with mean μ...
Let X1 , . . . , xn be n iid. random variables with distribution N (θ, θ) for some unknown θ > 0. In the last homework, you have computed the maximum likelihood estimator θ for θ in terms of the sample averages of the linear and quadratic means, i.e. Xn and X,and applied the CLT and delta method to find its asymptotic variance. In this problem, you will compute the asymptotic variance of θ via the Fisher Information....
0Let X1, ....., Xn be iid Random variable from a Uniform distribution with pdf given by . (1) Is the 2-dimensional statistics T1(X) = (X(1), X(n)) a complete sufficient statistics? Justify your answer (2) Is the one-dimensional statistic a complete sufficient statistic? Justify your answer We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
A random variable X has probability density function f(x)=(a-1)x^(-a),for x>=1. (a) For independent observations x1,...,xn show that the log-likelihood is given by, l(a;x1,...,xn)=nlog(a-1)-a (b) Hence derive an expression for the maximum likelihood estimate for ↵. (c) Suppose we observe data such that n = 6 and 6 i=1 log(xi) = 12. Show that the associated maximum likelihood estimate for ↵ is given by aˆ ↵ =1 .5. logri We were unable to transcribe this image
Let X1, X2, ..., Xn be a random sample of size n from the distribution with probability density function To answer this question, enter you answer as a formula. In addition to the usual guidelines, two more instructions for this problem only : write as single variable p and as m. and these can be used as inputs of functions as usual variables e.g log(p), m^2, exp(m) etc. Remember p represents the product of s only, but will not work...