Question

Consider a multivariate random sample X1, . . . , Xnwhich comes from p-dimensional multivariate distribution...

Consider a multivariate random sample X1, . . . , Xnwhich comes from p-dimensional multivariate distribution N(µ, Σ), with mean vector µ ∈ R p and the variance-covariance positive definite matrix Σ. Find the distribution and its parameters for the matrix nXTHX where H is the idempotent centering matrix H = In −1/n (1n ⊗ 1n ) and 1n is the n-dimensional vector of all 1’s.

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Review: Manipulating Multivariate Gaussians 1 point possible (graded) Recall that a multivariate Gaussian N(μ⃗ ,Σ) is...
Review: Manipulating Multivariate Gaussians 1 point possible (graded) Recall that a multivariate Gaussian N(μ⃗ ,Σ) is a random vector Z=[Z(1),…,Z(n)]T where Z(1),…,Z(n) are jointly Gaussian , meaning that the density of Z is given by the joint pdf f:Rn → R Z ↦ 1(2π)n/2det(Σ)−−−−−−√exp(−12(Z−μ⃗ )TΣ−1(Z−μ⃗ )) where μ⃗ i =E[Z(i)],(vector mean). Σij =Cov(Z(i),Z(j))(positive definite covariance matrix). Suppose that Z∼N(0,Σ). Let M denote an n×n matrix. What is the distribution of MZ?
Problem 3.2 Let H ∈ Rn×n be symmetric and idempotent, hence a projection matrix. Let x...
Problem 3.2 Let H ∈ Rn×n be symmetric and idempotent, hence a projection matrix. Let x ∼ N(0,In). (a) Let σ > 0 be a positive number. Find the distribution of σx. (b) Let u = Hx and v = (I −H)x and find the joint distribution of (u,v). 1 (c) Someone claims that u and v are independent. Is that true? (d) Let µ ∈ Im(H). Show that Hµ = µ. (e) Assume that 1 ∈ Im(H) and find...
Suppose X1, · · · , Xn from a normal distribution N(µ, σ2 ) where µ...
Suppose X1, · · · , Xn from a normal distribution N(µ, σ2 ) where µ is unknown but σ is known. Consider the following hypothesis testing problem: H0 : µ = µ0 vs. Ha : µ > µ0 Prove that the decision rule is that we reject H0 if X¯ − µ0 σ/√ n > Z(1 − α), where α is the significant level, and show that this is equivalent to rejecting H0 if µ0 is less than the...
Exercise 4. Suppose that X = (X1, · · · , Xn) is a random sample...
Exercise 4. Suppose that X = (X1, · · · , Xn) is a random sample from a normal distribution with unknown mean µ and known variance σ^2 . We wish to test the following hypotheses at the significance level α. Suppose the observed values are x1, · · · , xn. For each case, find the expression of the p-value, and state your decision rule based on the p-values a. H0 : µ = µ0 vs. Ha : µ...
Suppose that we have a random sample X1, ** , Xn drawn from a distribution that...
Suppose that we have a random sample X1, ** , Xn drawn from a distribution that only takes positive values. Suppose that the sample size n is sufficiently large. Consider the new random variable ∏ n i=1 Xi . Derive the distribution of this new random variable and explain your reasoning mathematically
Suppose that we have a random sample X1, · · , Xn drawn from a distribution...
Suppose that we have a random sample X1, · · , Xn drawn from a distribution that only takes positive values. Suppose that the sample size n is sufficiently large. Consider the new random variable ∏ n i=1 Xi . Derive the distribution of this new random variable and explain your reasoning mathematically
Let X1, X2, . . . , Xn be a random sample of size n from...
Let X1, X2, . . . , Xn be a random sample of size n from a distribution with variance σ^2. Let S^2 be the sample variance. Show that E(S^2)=σ^2.
Let X1, X2, . . . , Xn be a random sample from the normal distribution...
Let X1, X2, . . . , Xn be a random sample from the normal distribution N(µ, 36). (a) Show that a uniformly most powerful critical region for testing H0 : µ = 50 against H1 : µ < 50 is given by C2 = {x : x ≤ c}. Find the values of c for α = 0.10.
Suppose X1, . . . , Xn are a random sample from a N(0, σ^2) distribution....
Suppose X1, . . . , Xn are a random sample from a N(0, σ^2) distribution. Find the MLE of σ^2 and show that it is an unbiased efficient estimator.
Consider the bivariate random vector x = x1 x2 ∼ N2 µ1 µ2 , σ 2...
Consider the bivariate random vector x = x1 x2 ∼ N2 µ1 µ2 , σ 2 1 ρσ1σ2 ρσ1σ2 σ 2 2 1. Expand the matrix form of the density function to get the usual bivariate normal density involving σ1, σ2, ρ and exponential terms in (x1 1µ1) 2 ,(x1 1µ2) and (x2 2µ2) 2 . 2. Explain what happens in the following scenarios: (a) ρ = 0 (b) ρ = 1 (c) ρ = =1 1