Lecture 8: Convergence of transformations and law of large numbers

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1 Lecture 8: Covergece of trasformatios ad law of large umbers Trasformatio ad covergece Trasformatio is a importat tool i statistics. If X coverges to X i some sese, we ofte eed to check whether g(x ) coverges to g(x) i the same sese. The cotiuous mappig theorem provides a aswer to the questio i may problems. Theorem Cotiuous mappig theorem Let X,X 1,X 2,... be radom k-vectors defied o a probability space ad g be a measurable fuctio from (R k,b k ) to (R l,b l ). Suppose that g is cotiuous a.s. P X. The (i) X a.s. X implies g(x ) a.s. g(x); (ii) X p X implies g(x ) p g(x); (iii) X d X implies g(x ) d g(x). UW-Madiso (Statistics) Stat 709 Lecture / 15

2 Proof (i) ca be established usig a result i calculus. (iii) follows from Theorem 1.9(i): for ay bouded ad cotiuous h, E[h(g(X ))] E[h(g(X))], sice h g is bouded ad cotiuous. We prove (ii) for the special case of X = c (a costat). From the cotiuity of g, for ay ε > 0, there is a δ ε > 0 such that Hece, ad g(x) g(c) < ε wheever x c < δ ε. {ω : g(x (ω)) g(c) < ε} {ω : X (ω) c < δ ε } P( g(x ) g(c) ε) P( X c δ ε ). Hece g(x ) p g(c) follows from X p c. Is the previous arguemet still valid whe c is replaced by the radom vector X i the geeral case? If ot, how do we fix the proof? UW-Madiso (Statistics) Stat 709 Lecture / 15

3 Example (i) Let X 1,X 2,... be radom variables. If X d X, where X has the N(0,1) distributio, the X 2 d Y, where Y has the chi-square distributio χ 2 1. (ii) Let (X,Y ) be radom 2-vectors satisfyig (X,Y ) d (X,Y ), where X ad Y are idepedet radom variables havig the N(0, 1) distributio. The X /Y d X/Y, which has the Cauchy distributio C(0,1). (iii) Uder the coditios i part (ii), max{x,y } d max{x,y }, which has the c.d.f. [Φ(x)] 2 (Φ(x) is the c.d.f. of N(0,1)). I Example 1.30(ii) ad (iii), the coditio that (X,Y ) d (X,Y ) caot be relaxed to X d X ad Y d Y (exercise); i.e., we eed the covergece of the joit c.d.f. of (X,Y ). This is differet whe d is replaced by p or a.s.. The ext result, which plays a importat role i statistics, establishes the covergece i distributio of X + Y or X Y whe o iformatio regardig the joit c.d.f. of (X,Y ) is provided. UW-Madiso (Statistics) Stat 709 Lecture / 15

4 Theorem 1.11 (Slutsky s theorem) Let X,X 1,X 2,..., Y 1,Y 2,... be radom variables o a probability space. Suppose that X d X ad Y p c, where c is a costat. The (i) X + Y d X + c; (ii) Y X d cx; (iii) X /Y d X/c if c 0. Proof We prove (i) oly. The proofs of (ii) ad (iii) are left as exercises. Let t R ad ε > 0 be fixed costats. The F X +Y (t) = P(X + Y t) P({X + Y t} { Y c < ε}) + P( Y c ε) P(X t c + ε) + P( Y c ε) UW-Madiso (Statistics) Stat 709 Lecture / 15

5 Proof (cotiued) Similarly, F X +Y (t) P(X t c ε) P( Y c ε). If t c, t c + ε, ad t c ε are cotiuity poits of F X, the it follows from the previous two iequalities ad the hypotheses of the theorem that F X (t c ε) limif Sice ε ca be arbitrary (why?), F X +Y (t) limsupf X +Y (t) F X (t c + ε). lim F X +Y (t) = F X (t c). The result follows from F X+c (t) = F X (t c). A applicatio of Theorem 1.11 is give i the proof of the followig importat result. UW-Madiso (Statistics) Stat 709 Lecture / 15

6 Theorem 1.12 Let X 1,X 2,... ad Y = (Y 1,...,Y k ) be radom k-vectors satisfyig a (X c) d Y, where c R k ad {a } is a sequece of positive umbers with lim a =. Let g be a fuctio from R k to R. (i) If g is differetiable at c, the a [g(x ) g(c)] d [ g(c)] τ Y, where g(x) deotes the k-vector of partial derivatives of g at x. (ii) Suppose that g has cotiuous partial derivatives of order m > 1 i a eighborhood of c, with all the partial derivatives of order j, 1 j m 1, vaishig at c, but with the mth-order partial derivatives ot all vaishig at c. The a m 1 [g(x ) g(c)] d m! k i 1 =1 k i m =1 m g x i1 x Y i1 Y im im x=c UW-Madiso (Statistics) Stat 709 Lecture / 15

7 Proof We prove (i) oly. Let Z = a [g(x ) g(c)] a [ g(c)] τ (X c). If we ca show that Z = o p (1), the by a (X c) d Y, Theorem 1.9(iii), ad Theorem 1.11(i), result (i) holds. The differetiability of g at c implies that for ay ε > 0, there is a δ ε > 0 such that g(x) g(c) [ g(c)] τ (x c) ε x c wheever x c < δ ε. The for a fixed η > 0, P( Z η) P( X c δ ε ) + P(a X c η/ε). Sice a, a (X c) d Y ad Theorem 1.11(ii) imply X p c. By Theorem 1.10(iii), a (X c) d Y implies a X c d Y. Without loss of geerality, we ca assume that η/ε is a cotiuity poit of F Y. UW-Madiso (Statistics) Stat 709 Lecture / 15

8 Proof (cotiued) The lim supp( Z η) lim P( X c δ ε ) + lim P(a X c η/ε) = P( Y η/ε). Z p 0 follows sice ε ca be arbitrary. Remarks I statistics, we ofte eed a odegeerated limitig distributio of a [g(x ) g(c)] so that probabilities ivolvig a [g(x ) g(c)] ca be approximated by the c.d.f. of [ g(c)] τ Y, uder Theorem 1.12(i). Whe g(c) = 0, Theorem 1.12(i) idicates that the limitig distributio of a [g(x ) g(c)] is degeerated. I such cases the result i Theorem 1.12(ii) may be useful. UW-Madiso (Statistics) Stat 709 Lecture / 15

9 Corollary 1.1 (the δ-method) Assume the coditios of Theorem If Y has the N k (0,Σ) distributio, the Example 1.31 a [g(x ) g(c)] d N (0,[ g(c)] τ Σ g(c)). Let {X } be a sequece of radom variables satisfyig (X c) d N(0,1). Cosider the fuctio g(x) = x 2. If c 0, the a applicatio of Corollary 1.1 gives that (X 2 c 2 ) d N(0,4c 2 ). If c = 0, g (c) = 0 but g (c) = 2. Hece, a applicatio of Theorem 1.12(ii) gives that X 2 d [N(0,1)] 2, which has the chi-square distributio χ1 2 (Example 1.14). The last result ca also be obtaied by applyig Theorem 1.10(iii). UW-Madiso (Statistics) Stat 709 Lecture / 15

10 Example: Ratio estimator (X 1,Y 1 ),...,(X,Y ) are iid bivariate radom vectors with fiite 2d order momemts X = 1 i X i, Ȳ = 1 i Y i µ x = E(X 1 ), µ y = E(Y 1 ) 0, σ 2 x =Var(X 1 ), σ 2 y =Var(Y 1 ), σ xy =Cov(X 1,Y 1 ) By the CLT, (( X Ȳ ) ( µx µ y )) ( ( σ 2 d N 2 0, x σ xy σ xy σy 2 )) By the δ-method, g(x,y) = x/y, g/ x = y 1, g/ y = xy 2 ( X µ x Ȳ ( σ 2 = (µ y 1, µ x µ y 2 σ 2 ) x σ xy σ xy σy 2 µ y ) d N(0,σ 2 ) )( µ 1 y µ x µ 2 y ) = σ 2 x µ 2 y µ xσ xy µ 3 y + µ2 x σ 2 y µ y 4 UW-Madiso (Statistics) Stat 709 Lecture / 15

11 The law of large umbers The law of large umbers cocers the limitig behavior of a sum of radom variables. The weak law of large umbers (WLLN) refers to covergece i probability. The strog law of large umbers (SLLN) refers to a.s. covergece. The WLLN ad SLLN play importat roles i establishig cosistecy of estimators i large sample statistical iferece. Lemma 1.6. (Kroecker s lemma) Let x R, a R, 0 < a a +1, = 1,2,..., ad a. If the series =1 x /a coverges, the a 1 i=1 x i 0. Our first result gives the WLLN ad SLLN for a sequece of idepedet ad idetically distributed (i.i.d.) radom variables. UW-Madiso (Statistics) Stat 709 Lecture / 15

12 Theorem 1.13 Let X 1,X 2,... be i.i.d. radom variables. (i) (The WLLN). A ecessary ad sufficiet coditio for the existece of a sequece of real umbers {a } for which 1 X i a p 0 i=1 is that P( X 1 > ) 0, i which case we may take a = E(X 1 I { X1 }). (ii) (The SLLN). A ecessary ad sufficiet coditio for the existece of a costat c for which 1 X i a.s. c i=1 is that E X 1 <, i which case c = EX 1 ad 1 c i (X i EX 1 ) a.s. 0 i=1 for ay bouded sequece of real umbers {c i }. UW-Madiso (Statistics) Stat 709 Lecture / 15

13 Proof of Theorem 1.13(i) We prove the sufficiecy. The proof of ecessity ca be foud i Petrov (1975). Cosider a sequece of radom variables obtaied by trucatig X j s at : Y j = X j I { Xj }. Let T = X X, Z = Y Y. The P(T Z ) j=1 P(Y j X j ) = P( X 1 > ) 0. For ay ε > 0, it follows from Chebyshev s iequality that ( ) Z EZ P > ε var(z ) ε 2 2 = var(y 1) ε 2 EY 1 2 ε 2, where the last equality follows from the fact that Y j, j = 1,...,, are i.i.d. UW-Madiso (Statistics) Stat 709 Lecture / 15

14 Proof of Theorem 1.13(i) (cotiued) From itegratio by parts, we obtai that EY1 2 = 1 x 2 df X1 (x) = 2 xp( X 1 > x)dx P( X 1 > ), [0,] 0 which coverges to 0 sice P( X 1 > ) 0 (why?). This proves that (Z EZ )/ p 0, which together with P(T Z ) 0 ad the fact that EY j = E(X 1 I { X1 }) imply the result. Proof of Theorem 1.13(ii) The proof for sufficiecy is give i the textbook. We prove the ecessity. Suppose that T / a.s. holds for some c R, T = X X. The X = T c 1 ( T 1 1 c ) + c a.s. 0. UW-Madiso (Statistics) Stat 709 Lecture / 15

15 Proof of Theorem 1.13 (ii) From Exercise 114, X / a.s. 0 ad the i.i.d. assumptio o X s imply P( X ) = P( X 1 ) <, =1 =1 which implies E X 1 < (Exercise 54). From the proved sufficiecy, c = EX 1. Remarks If E X 1 <, the a = E(X 1 I { X1 }) EX 1 ad result the WLLN is actually established i Example 1.28 i a much simpler way. O the other had, if E X 1 <, the a stroger result, the SLLN, ca be obtaied. Some results for the case of E X 1 = ca be foud i Exercise 148 ad Theorem i Chug (1974). The ext result is for sequeces of idepedet but ot ecessarily idetically distributed radom variables. UW-Madiso (Statistics) Stat 709 Lecture / 15

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