MA 967 Homework 2
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MA 967
Homework 2
1. Exercise 4.5.3
Let X have a pdf of the form f (x; θ) = θx9 _1 , 0 < x < 1, zero elsewhere, where θ ← {θ : θ = 1, 2}. To test the simple hypothesis H0 : θ = 1 against the alternative simple hypothesis H1 : θ = 2, use a random sample X1 , X2 of size n = 2 and define the critical region to be C = {(x1 , x2 ) : ≤ x1 x2 }. Find the power function of the test.
2. Exercise 5.1.5
Let X1 , . . . , Xn be iid random variables with common pdf
,e_(z_9) x > θ, -& < θ < &
This pdf is called the shifted exponential. Let Yn = min{X1 , . . . , Xn }. Prove that Yn → θ in probability, by first obtaining the cdf of Yn .
3. Exercise 5.1.7
For Exercise 5.1.5, obtain the mean of Yn . Is Yn an unbiased estimator of θ? Obtain an unbiased estimator of θ based on Yn ,
4. Exercise 5.2.2
Let Y1 denote the minimum of a random sample of size n from a distribution that has pdf f (x) = e_(z_9), θ < x < &, zero elsewhere. Let Zn = n(Y1 - θ). Investigate the limiting distribution of Zn .
5. Exercise 5.2.4
Let Y2 denote the second smallest item of a random sample of size n from a distribution of the continuous type that has cdf F (x) and pdf f (x) = F\ (x). Find the limiting distribution of Wn = nF (Y2 ).
6. Exercise 5.3.13
Using the notation of Example 5.3.5, show that equation (5.3.3) is true.
pˆ - p d
′pˆ(1 - pˆ)/n
7. Exercise 6.1.1
Let X1 , X2 , . . . , Xn be a random sample from a Γ(α = 3, β = θ) distribution, 0 < θ < &. Determine the mle of θ .
8. Exercise 6.1.9
Let X1 , X2 , . . . , Xn be a random sample from a Bernoulli distribution with parameter p. If p is restricted so that we know that ≤ p ≤ 1, find the mle of this parameter.
9. Exercise 6.1.11
Let X1 , X2 , . . . , Xn be a random sample from the Poisson distribution with 0 < θ ≤ 2. Show that the mle of θ is θˆ = min{ , 2}.
10. Exercise 6.2.1
Prove that , the mean of a random sample of size n from a distribution that is N (θ, σ2 ), & < θ < &, is, for every known σ 2 > 0, an efficient estimator of θ .
11. Exercise 6.2.7
Let X have a gamma distribution with α = 4 and β = θ > 0.
(a) Find the Fisher Information I(θ).
(b) If X1 , X2 , . . . , Xn is a random sample from this distribution, show that the mle of θ is an efficient estimator of θ .
(c) What is the asymptotic distribution of ^n(θˆ - θ)?
12. Exercise 6.2.8
Let X be N (0, θ), 0 < θ < &.
(a) Find the Fisher Information I(θ).
(b) If X1 , X2 , . . . , xn is a random sample from this distribution, show that the mle of θ is an efficient estimator of θ .
(c) What is the asymptotic distribution of ^n(θˆ - θ)?
2022-07-12