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수리통계학 시험은 단순한 계산 능력을 평가하는 시험이 아니라, 확률과 통계를 이론적으로 얼마나 깊이 이해하고 있는지를 측정하는 과정이다. 수리통계학은 통계학의 수학적 기초를 다루는 학문으로, 확률이론을 바탕으로 추정, 검정, 분포이론 등을 체계적으로 전개한다. 따라서 시험 역시 공식 암기보다는 개념의 구조를 이해하고 이를 논리적으로 전개하는 능력을 요구한다.
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Oct. 22, 2018
1.(50) Let X 1 , · · · , X 25 be r.s. from N (μ, 1). Consider a test of size α = 0. 05 for testing H 0 : μ = 0 vs H 1 : μ > 0. Compute power of the test when μ = 0.329 (Hint : z. 05 = 1.645).
(Sol) Under H 0 , Z = (^) σ/X¯−√μn = (^1) /X√¯ 25 ∼ N (0, 1), and we reject if Z > z. 05 =
1 .645. Now, the power at μ = 0.329 is P ( 0 X¯. 2 > 1 .645) = P ( X¯− 00 .. 2329 > 1. 645 −
2.(180) Let X ∼ Exp(λ), i.e., the pdf of X is f (x) = (^1) λ e−x/λI(x > 0). (1)(40) Find the pdf of T = 1 − e−x/λ. (Sol) The inverse function is x = −λlog(1 − t), and Jacobian is λ/(1 − t). Hence the pdf of T is g(t) = f (−λlog(1 − t))|J| = I(0 < t < 1). (2)(50) Let X 1 , · · · , Xn be r.s. from Exp(λ), and Y 1 < · · · < Yn be the order statistics of X i′ s. Let W 1 = nY 1 , W 2 = (n − 1)(Y 2 − Y 1 ), W 3 = (n − 2)(Y 3 − Y 2 ), · · · , Wn = Yn − Yn− 1. Show that W 1 , W 2 , · · · , Wn are iid Exp(λ). (Sol) The inverse functions are y 1 = (^1) n w 1 , y 2 = (^) n^1 w 1 + (^) n−^11 w 2 , · · · , yn = 1 n w^1 +^
1 n− 1 w^2 +^ · · ·^ +^ wn, and^ |J|^ = 1/n!. Hence, the jpdf of^ W^1 ,^ · · ·^ , Wn^ is g(w 1 , · · · , wn) = f ( (^) n^1 w 1 , (^1) n w 1 + (^) n−^11 w 2 , · · ·)/n! = n!λ−ne−^
∑ (^) wi/λ /n! = ∏^ λ^1 e−wi/λ. (3)(50) Show that 2 ∑ni=1 Wi/λ ∼ χ^2 (r), and find r. (Sol) Note that Wi = Γ(1, λ), so that V ≡ ∑^ Wi ∼ Γ(n, λ). Let Z = 2V /λ, i.e., v = λz/2, and |J| = λ/2. Hence, the pdf of Z is g(z) = f (λz/2)|J| = z 22 n −^1 e−z/^2 Γ( 22 n )2^2 n/^2 ∼^ χ
(^2) (2n).
(4)(40) Using the result of (3), find 100(1 − α)% C.I. for λ. (Sol) Since 2v/λ ∼ χ^2 (2n), we have 1 − α = P (χ^21 − α 2 ≤ (^2) λv ≤ χ^2 α 2 ). Hence, 100(1 − α)% C.I. for λ is (2v/χ^2 α 2 , 2 v/χ^21 − α 2 ).
3.(90) Let X 1 , · · · , X 10 be r.s. from P oisson(θ). (1)(50) Consider a test H 0 : θ = 0.2 vs H 1 : θ > 0 .2. Find a randomized test of size α = 0.05. (When Y ∼ P oisson(2), P (Y ≤ 4) = 0. 947 , P (Y ≤ 5) = 0 .983). (Sol) Note that Y = ∑^10 i=1 Xi ∼ P oisson(2), and P (Y ≥ 5) = 0. 053 , P (Y ≥
4.(50) Let X 1 , · · · , Xn be r.s. from U (θ, 1), θ > 0. Find the MLE of θ. (Sol) The likelihood is L(θ) = ∏(1 − θ)−^1 I(θ ≤ Xi ≤ 1) = (1 − θ)−nI(θ ≤ X(1)). Now, (1 − θ)−n^ is maximized at X(1) when 0 ≤ θ ≤ X(1).
5.(50) Let X 1 , · · · , Xn be r.s. from U (θ − 0. 5 , θ + 0.5). Find the MLE of θ. (Sol) The likelihood is L(θ) = ∏^ I(θ − 0. 5 ≤ Xi ≤ θ + 0.5) = I(θ − 0. 5 ≤ X(1) ≤ X(n) ≤ θ + 0.5) = I(X(n) − 0. 5 ≤ θ ≤ X(1) + 0.5). Hence, the MLE of θ is any value between X(n) − 0 .5 and X(1) + 0.5.
6.(80) Let X 1 , · · · , Xn be r.s. from a distribution with pdf f (x; θ), θ ∈ R^1. Let Yi = ∂logf (Xi; θ)/∂θ, Zi = ∂^2 logf (Xi; θ)/∂θ^2 , i = 1, · · · , n. (1)(40) Show that
n( Y¯ − k 1 ) converges in distribution to N (0, k 2 ), and find k 1 and k 2. (Sol) By the CLT, k 1 = E(Y 1 ) = 0 and V ar(Y 1 ) = I(θ). (2)(40) Show that Z¯ converges in probability to k 3 , and find k 3. (Sol) By the WLLN, k 2 = E(Z 1 ) = −I(θ).