Exercise 4.3 Use the Continuity Theorem to prove the Cramér-Wold Theorem, Theorem. (1) φ a X(1).
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1 Assigmet 7 Exercise 4.3 Use the Cotiuity Theorem to prove the Cramér-Wold Theorem, Theorem Hit: a X d a X implies that φ a X (1) φ a X(1). Sketch of solutio: As we poited out i class, the oly tricky part of the Cramér-Wold Theorem is showig that if a X d a X for all a, the X d X. But i this case, the Cotiuity Theorem states that φ a X (t) φ a X(t) for all t R. I particular, lettig t 1, we obtai φ X (a) φ X (a). Sice this is true for all a, we obtai oce agai from the Cotiuity Theorem that X d X. Exercise 4.4 Suppose X N k (µ, Σ), where Σ is ivertible. Prove that (X µ) Σ 1 (X µ) χ 2 k. Hit: If Q diagoalizes Σ, say QΣQ Λ, let Λ 1/2 be the diagoal, oegative matrix satisfyig Λ 1/2 Λ 1/2 Λ ad cosider Y Y, where Y (Λ 1/2 ) 1 Q(X µ). Sketch of solutio: Usig the hit, we obtai Y Y (X µ) Σ 1 (X µ). But with X N k (µ, Σ), we obtai Y N k (, I) because Var Y (Λ 1/2 ) 1 QΣQ (Λ 1/2 ) 1 (Λ 1/2 ) 1 Λ(Λ 1/2 ) 1 I. This proves the result, sice we see that Y Y is just the sum of k idepedet squared stadard ormal radom variables. Exercise 4.5 Let X 1, X 2,... be idepedet Poisso radom variables with mea λ 1. Defie Y (X 1). (a) Fid E (Y + ), where Y + Y I{Y > }. Sketch of solutio: Notig that Y Y + Y ad E Y, E Y. Thus, we ca fid E Y istead of E Y + we see that E Y + We shall use the fact that X is Poisso with mea. [ { (X E (Y ) E 1) } ] 1 E [ (X ) ] 236.
2 1 i e [ e ip (X i) 1 ] k k! k (k 1)! k [ k e.! k1 k 1 k! k k! k ] ( k)p (X k) (b) Fid, with proof, the limit of E (Y + ) ad prove Stirlig s formula Hit: Use the result of Exercise 3.12.! 2π +1/2 e. k Sketch of solutio: By the cetral limit theorem, Y d N(, 1). Sice E Y 2 1 for all, Exercise 3.11 (with ɛ 1) shows that the Y are uiformly itegrable, ad thus (by Exercise 3.1) E Y E Z, where Z is stadard ormal. By a similar argumet, E Y E Z. But Y + Y + Y, so we coclude that E Y + E Z + E Z 1/ 2π. Combiig this with the result of part (a), we obtai e! 1 2π, which is equivalet to 2πe! 1, which is exactly the cotet of Stirlig s approximatio as experssed i Example Exercise 4.14 Let (a 1,..., a ) be a radom permutatio of the itegers 1,...,. If a j < a i for some i < j, the the pair (i, j) is said to form a iversio. Let X be the total umber of iversios: X I{a j < a i }. j2 237
3 For example, if 3 ad we cosider the permutatio (3, 1, 2), there are 2 iversios sice 1 a 2 < a 1 3 ad 2 a 3 < a 1 3. This problem asks you to fid the asymptotic distributio of X. (a) Defie Y 1 ad for j > 1, let Y j I{a j < a i } be the umber of a i greater tha a j to the left of a j. The the Y j are idepedet (you do t have to show this; you may wish to thik about why, though). Fid E (Y j ) ad Var Y j. Sketch of solutio: By symmetry, the evet a j < a i is just as probable as the evet a i < a j, which meas that the probability of this evet is 1/2. Therefore, E Y j P (a j < a i ) 1 2 j 1 2. For the variace of Y j, we eed the fact that for i k, P (a j < a i ad a j < a k ) P (a j is the smallest of {a j, a i, a k }) 1 3, agai by symmetry (because the smallest is just as likely to be ay of a j, a i, or a k ). Thus, we obtai Var Y j Cov (I{a j < a i }, I{a j < a k }) k1 Var I{a j < a i } i<k j 1 [ j ] 4 3(j 1) (j 1)(j 2) j [P (a j < a i ad a j < a k ) P (a j < a i )P (a j < a k )] Notice that the above mea ad variace are also correct for Y 1, which is defied as the costat. 238
4 (b) Use X Y 1 + Y Y to prove that 3 4X d 1 N(, 1). 2 2 Sketch of solutio: that s 2 j1 j Let us verify the Lideberg coditio. Notice ( + 1)(2 + 1) 6(12) 6 6(12) (2 + 5)( 1) Furthermore, otice that Y j E Y j j 1 < for all j because Y j may oly take values from through j 1. Therefore, 1 s 2 E [ (Y j E Y j ) 2 I{ Y j E Y j ɛs } ] 1 s 2 j1 E [ (Y j E Y j ) 2 I{ ɛs } ] j1 I{ ɛs }. But sice s 3 /36, we kow that I{ ɛs } will be idetically zero for large eough, o matter how small the fixed value of ɛ is. This meas that the Lideberg coditio is satisfied, so we coclude that We may check that X E X s d N(, 1). E X j 1 2 ( + 1) 4 2 ( 1). 4 Thus, with a bit of algebra, we obtai [ X E X 3 3 4X ] s 36s Sice 36s / 3 1 ad 3/2 as, we coclude by Slutsky s theorem that 3 4X d 1 1 Z +, 2 2 where Z is stadard ormal, which proves the result. 239
5 (c) For 1, evaluate the distributio of iversios as follows. First, simulate 1 permutatios o {1, 2,..., 1} ad for each permutatio, cout the umber of iversios. Plot a histogram of these 1 umbers. Use the results of the simulatio to estimate P (X 1 24). Secod, estimate P (X 1 24) usig a ormal approximatio. Ca you fid the exact iteger c such that 1!P (X 1 24) c? Sketch of solutio: Below is a R fuctio that couts the umber of iversios i a vector (by default a permutatio of 1 elemets), alog with code to test it o 1 permutatios of 1 elemets. iv <- fuctio(asample(), 1) { m <- outer(a,a,"<") sum(m & lower.tri(m)) } s <- replicate(1, iv()) sum(s<24) [1] 61 My simulatio produced 61/1 permutatios with 24 or fewer iversios. Below is a histogram alog with the code that created it. Percet of Total pdf("iv.pdf") hist(s,mai"",ylab"percet of Total", class3,col5,yaxt"") axis(2,at1*(:8),labels:8) dev.off() s 24
6 From part (b), we kow that the exact theoretical mea ad stadard deviatio for X 1 are 9/ ad 225/ , respectively. Thus, usig a cotiuity correctio, we fid the ormal approximatio P (X 24) Φ([ ]/5.59).64. We could also use the ormal approximatio proved to be asymptotically valid i part (b), which gives approximate mea 1/4 25 ad stadard deviatio 1/6 5.27, which gives as the ormal approximatio P (X 24) Φ([ ]/5.27).462. (Note how far apart the two ormal approximatios are i this case despite the fact that they are asymptotically equivalet!) By the way, the exact theoretical value of P (X 24) is / If K,i deotes the umber of permutatios o elemets that cotai exactly i iversios, the I do t kow of ay closed-form expressio for K,i, but there s a fairly simple recursio you might wat to verify (which is how I obtaied the umber ): K,i 1 K 1,i j for i >, >, j where K, 1 for all 1 ad K,i wheever i < or. Suppose that X 1, X 2, X 3 is a sample of size 3 from a beta (2, 1) dis- Exercise 4.15 tributio. (a) Fid P (X 1 + X 2 + X 3 1) exactly. Sketch of solutio: The desity fuctio of a beta (2, 1) distributio is 2x 2 1 (1 x) 1 1 2x for x (, 1). Thus, the probability is just the triple itegral of the joit desity fuctio over the regio {x 1 + x 2 + x 3 1}: 1 1 x1 1 x1 x 2 8x 1 x 2 x 3 dx 3 dx 2 dx x x 1 x 2 (1 x 1 x 2 ) 2 dx 2 dx 1 x1 4x x 3 1 4x x 5 1 dx1 As a decimal approximatio, 1/ (b) Fid P (X 1 + X 2 + X 3 1) usig a ormal approximatio derived from the cetral limit theorem. 241
7 Sketch of solutio: The mea of the beta(2, 1) distributio is 2/3 ad the variace is 1/18. Thus, the cetral limit theorem approximatio says that X 1 +X 2 +X 3 is distributed approximately ormally with mea 2 ad variace 1/6. Stadardizig gives 1 2 P (X 1 + X 2 + X 3 1) Φ.715 1/6 (c) Let Z I{X 1 + X 2 + X 3 1}. Approximate E Z P (X 1 + X 2 + X 3 1) by Z 1 Z i/1, where Z i I{X i1 + X i2 + X i3 1} ad the X ij are idepedet beta (2, 1) radom variables. I additio to Z, report Var Z for your sample. (To thik about: What is the theoretical value of Var Z?) Sketch of solutio: I got 8/1.8 values of X 1 + X 2 + X 3 less tha 1 i my simulatio, code for which is show below. This gives a sample variace of.794. Sice Z is a Beroulli radom variable with probability 1/9, the theoretical variace is 89/ sum(apply(matrix(rbeta(3,2,1),row1),1,sum)<1) [1] 8 (d) Approximate P (X 1 + X 2 + X 3 3 ) usig the ormal approximatio ad 2 the simulatio approach. (Do t compute the exact value, which is more difficult to tha i part (a); do you see why?) Sketch of solutio: With the simulatio, I got 125/1.125 as the poit estimate. The ormal approximatio gives Φ /6 The triple itegral i this case is much harder! 242
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