Question

In: Statistics and Probability

Let X1, X2, . . . , Xn iid∼ N (µ, σ2 ). Consider the hypotheses...

Let X1, X2, . . . , Xn iid∼ N (µ, σ2 ).

Consider the hypotheses H0 : µ = µ0 and H1 : µ (not equal)= µ0 and the test statistic (X bar − µ0)/ (S/√ n). Note that S has been used as σ is unknown.

a. What is the distribution of the test statistic when H0 is true?

b. What is the type I error of an α−level test of this type? Prove it.

c. What is the type II error of an α−level test of this type? Prove it.

d. Describe the properties of the type II error function for this test.

e. How can a 100(1 − α)% confidence interval be used to carry out this test? Prove it.

Solutions

Expert Solution


Related Solutions

Let X1, . . . , Xn ∼ iid N(θ, σ2 ), with one-sided hypotheses H0...
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 following a uniform distribution over the...
Let X1, X2, . . . , Xn be iid following a uniform distribution over the interval (θ, 2θ) (θ > 0). (a) Find a method of moments estimator of θ. (b) Find the MLE of θ. (c) Find a constant k such that E(k ˆθ) = θ. (d) By using the Rao-Blackwell, which estimators of (a) and (b) can be improved?
Let X = ( X1, X2, X3, ,,,, Xn ) is iid, f(x, a, b) =...
Let X = ( X1, X2, X3, ,,,, Xn ) is iid, f(x, a, b) = 1/ab * (x/a)^{(1-b)/b} 0 <= x <= a ,,,,, b < 1 then, find a two dimensional sufficient statistic for (a, b)
Let X1, X2, . . . , Xn be iid Poisson random variables with unknown mean...
Let X1, X2, . . . , Xn be iid Poisson random variables with unknown mean µ 1. Find the maximum likelihood estimator of µ 2.Determine whether the maximum likelihood estimator is unbiased for µ
Let X1, . . . , Xn ∼ iid Exp(θ) and consider the test for H0...
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...
3. Let X1...Xn be N(μX,σ) and Y1...Yn be iid N(μy,σ) with the two samples X1...Xn, and...
3. Let X1...Xn be N(μX,σ) and Y1...Yn be iid N(μy,σ) with the two samples X1...Xn, and Y1...Xn independent of each other. Assume that the common population SD σ is known but the two means are not. Consider testing the hypothesis null: μx = μy vs alternative: μx ≠ μy. d. Assume σ=1 and n=20. How large must δ be for the size 0.01 test to have power at least 0.99? e. Assume σ=1and δ=0.2. How large must n be for...
3. Let X1...Xn be N(μX,σ) and Y1...Yn be iid N(μy,σ) with the two samples X1...Xn, and...
3. Let X1...Xn be N(μX,σ) and Y1...Yn be iid N(μy,σ) with the two samples X1...Xn, and Y1...Xn independent of each other. Assume that the common population SD σ is known but the two means are not. Consider testing the hypothesis null: μx = μy vs alternative: μx ≠ μy. a. Find the likelihood ratio test statistic Λ. Specify which MLEs you are using and how you plug them in.
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 Poisson distribution with unknown mean µ. It is desired to test the following hypotheses H0 : µ = µ0         versus     H1 : µ not equal to µ0 where µ0 > 0 is a given constant. Derive the likelihood ratio test statistic
Let X1, X2, · · · , Xn be iid samples from density: f(x) = {θx^(θ−1),...
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, X2, · · · , Xn (n ≥ 30) be i.i.d observations from N(µ1,...
Let X1, X2, · · · , Xn (n ≥ 30) be i.i.d observations from N(µ1, σ12 ) and Y1, Y2, · · · , Yn be i.i.d observations from N(µ2, σ22 ). Also assume that X's and Y's are independent. Suppose that µ1, µ2, σ12 , σ22  are unknown. Find an approximate 95% confidence interval for µ1µ2.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT