Let X1, . . . , Xn ∼ iid Beta(θ, 1). (a) Find the UMP test...
Let X1, . . . , Xn ∼ iid Beta(θ, 1). (a) Find the UMP test for
H0 : θ ≥ θ0 vs H1 : θ < θ0. (b) Find the corresponding Wald
test. (c) How do these tests compare and which would you
prefer?
Let X1, . . . , Xn ∼ iid Exp(θ) and consider the test for H0 : θ
≥ θ0 vs H1 : θ < θ0.
(a) Find the size-α LRT. Express the rejection region in the
form of R = {X > c ¯ } where c will depend on a value from the χ
2 2n distribution.
(b) Find the appropriate value of c.
(c) Find the formula for the P-value of this test.
(d) Compare this test...
Let X1, . . . , Xn ∼ iid Unif(0, θ). (a) Is this family MLR in Y
= X(n)? (b) Find the UMP size-α test for H0 : θ ≤ θ0 vs H1 : θ >
θ0. (c) Find the UMP size-α test for H0 : θ ≥ θ0 vs H1 : θ < θ0.
(d) Letting R1 be the rejection region for the test in part (b) and
R2 be the rejection region for the test in part...
For a fixed θ>0, let X1,X2,…,Xn be i.i.d., each with the beta
(1,θ) density.
i) Find θ^ that is the maximum likelihood
estimate of θ.
ii) Let X have the beta (1,θ) density. Find the
density of −log(1−X). Recognize this as one of the famous ones and
provide its name and parameters.
iii) Find f that is the density of the MLE θ^
in part (i).
Let X1, . . . , Xn ∼ iid N(θ, σ2 ), with one-sided hypotheses H0
: θ ≤ θ0 vs H1 : θ > θ0. (a) If σ^2 is known, we can use the UMP
size-α test. Find the formula for the P-value of this test.
Let X1, X2, · · · , Xn be iid samples from density:
f(x) = {θx^(θ−1), if 0 ≤ x ≤ 1}
0 otherwise
Find the maximum likelihood estimate for θ. Explain, using
Strong Law of Large Numbers, that this maximum likelihood estimate
is consistent.
Let X1,…, Xn be a sample of iid N(0, ?) random
variables with Θ=(0, ∞). Determine
a) the MLE ? of ?.
b) E(? ̂).
c) the asymptotic variance of the MLE of ?.
d) the MLE of SD(Xi ) = √ ?.
Let X1,...,Xn be i.i.d. N(θ,1), where θ ∈ R is the
unknown parameter.
(a) Find an unbiased estimator of θ^2 based on
(Xn)^2.
(b) Calculate it’s variance and compare it with the Cram
́er-Rao lower bound.
Let X1,...,Xn ∼ Geo(θ).
(a) Find a 90% asymptotic confidence interval for θ.
(b) Find a 99% asymptotic lower confidence intervals for φ =
1/θ, the expected number of trials until the first success.
Let X1, ..., Xn be iid with pdf f(x; θ) = (1/ x√ 2π)
e(-(logx- theta)^2) /2 Ix>0 for θ ∈ R.
(a) (15 points) Find the MLE of θ.
(b) (10 points) If we are testing H0 : θ = 0 vs Ha :
θ != 0. Provide a formula for the likelihood ratio test statistic
λ(X).
(c) (5 points) Denote the MLE as ˆθ. Show that λ(X) is can be
written as a decreasing function of | ˆθ|...