. Prove that if a is a vector of constants with the same dimension as the...
Prove that any two finite-dimensional normed vector spaces of the same dimension are uniformly homeomorphic. In fact, show that we can even find a linear (and hence Lipschitz) homeomorphism between them.
7. Let A be an n × n matrix of constants, a and b be a n x 1 vectors of constants, and x be a n x 1 random vector with mean μ and nonsingular variance-covariance matrix V. Show the following (c) Var le"x] = aTya (d) Var [Az +b] = AVAT
For constants a and b, X and Y are random variables. Please prove that, var(aX + bY ) = a 2 var(X) + b 2 var(Y ) + 2abcov(X, Y ) If X and Y are uncorrelated, what will be the results?
Let X, , x, be a random sample from some density which has mean μ and variance σ2. Show that Σ a, X, is an unbiased estimator of/e for any set of known constants a, , . . . , a, satisfying Σ a,-1. If Σ a.-1, show that var [ Σ a, xl] is minimized for ai = 1/n, i = 1, [HINT: Prove that Σ a-Σ (al-IMF + 1/n when Σ al = 1 .] (a) (b) ,...
9. Find the dimension of each of the following vector spaces (a) The vector space of all diagonal n xn matrices. (b) The vector space of all symmetric n x n matrices. (c) The vector space of all upper triangular n x n matrices
9. Find the dimension of each of the following vector spaces (a) The vector space of all diagonal n xn matrices. (b) The vector space of all symmetric n x n matrices. (c) The vector space...
(3) Let X = (X1, X2) be a two-dimensional random vector with variance Var[X= 121 12] Compute Covſa, Xi +a X2, 6, X1 + b2 X2], where an, az, bi, by are given constants.
Let X,, X,,...X be a random sample of size n from a normal distribution with parameters a. Derive the Cramer-Rao lower bound matrix for an unbiased estimator of the vector of parameters (μ, σ2). b. Using the Cramer-Rao lower bound prove that the sample mean X is the minimum variance unbiased estimator of u Is the maximum likelihood estimator of σ--σ-->|··( X,-X ) unbiased? c.
Let X,, X,,...X be a random sample of size n from a normal distribution with...
Let at XW) =andom variable. Prove III. VARIANCE PROOFS (SINGLE RANDOM VARIABLE) Let S be a sample space. Let a, b, c be real numbers. a) Let X(W) = c for all w ES. Prove that Var(X) = 0. b) Let Y be a random variable. Prove that Var(Y + b) = Var(Y). c) Let Z be a random variable. Prove that Var(az) = a-Var(Z). d) Let W be a random variable. Use parts (b)-(c) to prove that Var(aW+b) =...
Problem 2. Problem 1 doesn't need to be done, it's here for
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166 Branching processes is a branching process whose .... is a branchin the result zes have mean μ (s l ) and variance σ 2, then var( ZnJun of Problem 9.6.1 to show that, if Zo. z 2. Use ditioning on the value of Zm, show th ose fa outition theorem and conditioning on the value of Z 9.6 Problems I. Let X1 , X2. . ....
Let X and Y be independent identically distributed random variables with means µx and µy respectively. Prove the following. a. E [aX + bY] = aµx + bµy for any constants a and b. b. Var[X2] = E[X2] − E[X]2 c. Var [aX] = a2Var [X] for any constant a. d. Assume for this part only that X and Y are not independent. Then Var [X + Y] = Var[X] + Var[Y] + 2(E [XY] − E [X] E[Y]). e....