2. Let Xi,... Xn be a random sample from the density f(x:0) 1o otherwise Suppose n...
2. Let X1,.n be a random sample from the density 0 otherwise Suppose n = 2m+ 1 for some integer m. Let Y be the sample median and Z = max(Xi) be the sample maximum (a) Apply the usual formula for the density of an order statistic to show the density of Y is (b) Note that a beta random variable X has density f(x) = TaT(可 with mean μ = α/(a + β) and variance σ2 = αβ/((a +s+...
3. Let Xi, , Xn be a random sample from a Poisson distribution with p.m.f Assume the prior distribution of Of λ is is an exponential with mean 1, i.e. the prior pdi g(A) e-λ, λ > 0 Note that the exponential distribution is a special gamma distribution; and a general gamma distribution with parameters α > 0 and β > 0 has the pd.f. h(A; α, β)-16(. otherwise Also the mean of a gamma random variable with the pd.f.h(Χα,...
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...
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).
2) (1 point) Let Xi, , Xn be a random sample from V(0, σ2). And their corresponding observations are x1, . . . , Xn. Show that is a (1-a) 100% confidence interval of σ2. 2) (1 point) Let Xi, , Xn be a random sample from V(0, σ2). And their corresponding observations are x1, . . . , Xn. Show that is a (1-a) 100% confidence interval of σ2.
Let XI, X2, , Xn İs a random sample from the probability density function Use factorization theorem to show that X(1) = min(X1 , . . . , Xn) is sufficient for θ Is X(1) minimal sufficient for θ? a. b.
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo 5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
7.2.6. Let X1, X2....Xn be a random sample of size n from a beta d with parameters α-θ and β statistic for θ 5. Show tha the product Xi X2 . . . Xn is a sufficient oherat tious is a sufficient statistic for
2. Let Xi, X,.., Xn denote a random sample from the probability density function Show that X(i) = min(X1,X2, . . . , Xn} is sufficient for ?. Hint: use an indictorfunction since the support depends on ?
2. Let Xi, X2, . Xn be a random sample from a distribution with the probability density function f(x; θ-829-1, 0 < x < 1,0 < θ < oo. Find the MLE θ