EFFECTIVE WLLN, SLLN, AND CLT IN STATISTICAL MODELS

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1 EFFECTIVE WLLN, SLLN, AND CLT IN STATISTICAL MODELS Ryszard Zieliński Ist Math Polish Acad Sc POBox 21, Warszawa 10, Polad ABSTRACT Weak laws of large umbers (W LLN), strog laws of large umbers (SLLN), ad cetral limit theorems (CLT ) i statistical models differ from those i probability theory i that they should hold uiformly i the family of distributios specified by the model If a limit law states that for every ε > 0 there exists N such that for all > N the iequalities ξ < ε are satisfied ad N = N(ε) is explicitly give tha we call the law effective It is trivial to obtai the effective statistical versio of W LLN i the Beroulli scheme, to get SLLN takes a little while, but CLT does ot hold uiformly Other statistical schemes are also cosidered Mathematics Subject Classificatio: 62E12, 62E20, 60F05, 60F15 Key words ad phrases: weak laws of large umbers, strog laws of large umbers, cetral limit theorems, statistical models, effective limit laws, uiform limit laws, Beroulli scheme, expoetial distributio, quatiles 1

2 1 THE BERNOULLI SCHEME Let X, X 1, X 2,, X, be iid radom variables with P θ X = 1 = P θ X = 0 = θ, θ (0, 1) ad let S = i=1 X i W LLN states that, uder every fixed θ (0, 1), S / θ i probability, which ca be writte i the form θ (0, 1) ε>0 η >0 N N P θ S θ > ε < η A appropriate N is give by the formula N = θ(1 θ)/ηε 2 I the related statistical model all what we kow about θ is that θ (0, 1) so that the above result is of o use: the statistical versio may be formulated as follows: propositio 1 (A) ε>0 η >0 N N θ (0, 1) P θ S θ > ε < η (B) the appropriate N = N(ε, η) = 1 4ηε 2 The formula is useful for example for costructig the cofidece iterval for a ukow θ, with a a priori postulated accuracy ad cofidece level Here ad further o Part (A) states the uiform covergece ad Part (B) makes the law effective Part (B) may be improved by the argumet used i the proof of Propositio 2 below (Berstei iequality) SLLN states that, uder every fixed θ (0, 1), S / θ as Usig the fact that ξ 0 as iff ε > 0 lim N P =N X > ε = 0 iff ε > 0 η > 0 N P =N X > ε < η, a appropriate effective statistical versio of the law takes o the form 2

3 propositio 2 (A) ε>0 η >0 N θ (0, 1) P θ =N S θ > ε < η; (B) the appropriate N =N(ε, η)=mi proof 4 ( η ( )) ε 2 log 1 e ε2 /4 1, 2 4ηε 2 By a rather crude estimatio oe obtais P θ S θ > ε < =N =N P θ S θ > ε ad the by the Berstei iequality for the Beroulli scheme (Serflig 1980, Jakubowski et al 2001) i the form P θ S θ > ε 2 /4 2e ε the followig estimatio holds P θ =N S θ > ε < 2e Nε2 /4 1 e ε2 /4 which eables us to explicitly fix N as ay iteger such that N > 4 ε 2 log ( η 2 ( 1 e ε2 /4 )) Table 1 (first lie) exhibits N = N(ε, η) for some ε ad η Aother formula for N may be costructed as follows (Weso lowski 2002) Defie Y i = X i θ, T k = 1 k k i=1 Y i, ad G k = σ(t k, T k+1, ) The (T k, G k+1 ) k=1,2, is a iverse martigale: E(T k G k+1 ) = E(T k T k+1 ) = 1 k k E(Y i T k+1 ) = T k+1, k = 1, 2, i=1 The maximal iequality for iverse martigales gives us P max T k a N k m V ar(t N ) a 2 = 3 θ(1 θ) Na 2 1 4Na 2

4 ad i cosequece P θ =N S θ > ε = P sup k N = lim m P T k ε max T k ε N k m 1 lim m 4Nε 2 = 1 4Nε 2 Now propositio 2(A) holds for ay N 1 4ε 2 η (secod lie i Table 1) Table 1 N(ε, η) η ε ,596 4,517 5, ,500 25, , , ,902 25, ,000 2,500, CLT for the Beroulli scheme holds for every θ (0, 1) separately, eve i a stroger versio ( uiformly i x ): θ (0, 1) Pθ sup S x Φ x ( x θ θ(1 θ) ) 0, as The classical CLT for the Beroulli scheme may be writte i the form θ x ε N = N(θ, x, ε) N S θ Pθ x θ(1 θ) Φ(x) ε 4

5 What statisticias eed is x ε N = N(x, ε) N θ S θ Pθ x Φ(x) ε θ(1 θ) or eve i a stroger form: uiformly i x The latter is however ot true To see that oe should prove that S θ x ε N N θ P θ x Φ(x) > ε θ(1 θ) It is sufficiet to prove that S θ x ε θ P θ x Φ(x) > ε θ(1 θ) To this ed take x = 0 ad ε = 1/4 The LHS = P θ S θ 1/2 If for ay fixed oe takes θ such that θ < 1 ad (1 θ) > 3/4, the P θ S θ = P θ S = 0 = (1 θ) > 3/4 ad LHS > ε It follows that CLT does ot hold uiformly i the statistical model with θ (0, 1) It is iterestig to observe that similar result holds i the iverse Biomial scheme (egative Biomial distributio) Let Y be the umber of experimets eeded to observe first success: P θ Y = y = (1 θ) y 1 θ, E θ Y = 1 θ, V ar θy = 1 θ θ 2 If Y, Y 1, Y 2, are iid ad T = i=1 Y i the P θ T θ 1 θ θ 2 x Φ(x) x=0 = P θ T Φ(0) θ > P θ T Φ(0) = θ 1 2 which teds to 1/2 as θ 1 Oe may coclude that typical difficulties i costructig cofidece itervals for θ (eg Brow et al 2001), based o ormal approximatio, arises from the fact that CLT does ot hold uiformly 5

6 2 EXPONENTIAL DISTRIBUTION If X 1, X 2, are iid radom variables with probability desity fuctio 1 e x/, x > 0, > 0, ad S = i=1 X i the the SLLN S / as does ot hold uiformly i > 0 ad the CLT x S P x Φ(x) 0 holds uiformly To prove the former it is eough to observe that for some fixed ε > 0, η > 0, ad for each, oe ca fid > 0 such that S P < ε < η which, by the fact that S / has gamma distributio Γ(, ) with the shape parameter ad the scale parameter /, easily follows from the followig estimatio S P < ε = 1 Γ() (1+ε/) (1 ε/) t 1 e t dt < 2ε 1 2π A stroger versio of the secod statemet may be formulated as the followig effective propositio 3 6

7 Before statig the theorem let us defie +x 1 t R(x, ) = 1 e t dt Φ(x) if x > Γ() 0 0 elsewhere propositio 3 If X 1, X 2, are iid radom variables with probability distributio fuctio 1 e x/ ad S = i=1 X i the (A) ε > 0 N = N(ε) > 0 sup P x S x Φ(x) < ε ad (B) a appropriate N = N(ε) is give umerically as a N such that max R(x, ) ε x proof To prove part (A) of the propositio it is eough to observe that P S x = P 1 S 1 + x which, due to the fact that (1/)(S /) is distributed as Γ(, 1/), does ot deped o : P S x = 1 Γ() +x 0 t 1 e t dt To prove part (B) observe that R(x, ) = P S x Φ(x) Fuctio R(x, ) is cotiuous ad bouded; two examples are exhibited i Fig 1 7

8 R(x, ) = =20 x -002 Fig1 Fuctio R(x,) Some values of R(x, ) preseted i Tab 2 below eable us to choose a proper N for typical values of ε; here x = arg mi x R(x, ) Tab 2 R(x, ) R(x, ) x , , , , , , , , , , ,132 17, , ,540 70, , ,224 Explicite formulas either for x or for R(x, ) are kow to the author 8

9 3 QUANTILES It is well kow (eg Serflig 1980) that if x q = x q (F ) is the uique quatile of order q of the distributio F ad k()/ q, the X k(): x q as Here X k: is the k-th order statistic from the sample X 1, X 2,, X The covergece is however ot uiform: for each ε, for each η, ad for every oe ca fid a distributio F with the uique quatile x q such that Xk(): P F x q > ε 1 η A ecessary ad sufficiet coditio for uiform covergece has bee give i Zieliński (1998) A effective uiform asymptotic theorem for a smaller class of model distributios may be stated as follows For a fixed q (0, 1), cosider the class F(q, ϑ) of all distributios F such that the desities f at the qth quatile x q exist ad they satisfy f(x q ) ϑ > 0 propositio 4 (A) ad (B) ε > 0 η > 0 N = N(ε, η) F F(q, ϑ) P F sup Xk(): x q > ε < η N ( ) 1 ( 1 8 log 1 exp 2 8 ϑ2 ε 2) η N(ϑ, ε, η) ϑ 2 ε 2 proof If δ = if F F miq F (x q ε), F (x q + ε) q for a class F of distributios, the for every F F P F sup Xk(): x q > ε < 2τ N N 1 τ with τ = exp δ 2 /2 (Serflig 1980) I the class F(q, ϑ) we have F (x q + t) q lim 0<t 0 t q F (x q t) = lim 0<t 0 t 9 = ϑ

10 so that there exists t 0 > 0 such that for all t < t 0 F (x q + t) q 1 2 ϑt ad q F (x q t) 1 2 ϑt ad i cosequece, for all sufficietly small ε (for ε < t 0 ) δ = miq F (x q ε), F (x q + ε) q 1 2 ϑε Now τ = exp δ 2 /2 exp 1 8 ϑ2 ε 2 Solvig, with respect to N, the equatio we obtai the result 2τ N 1 τ = η Table 3 below gives us a isight i how large samples are eeded to get the prescribed accuracy of the asymptotic Table 3 N(ϑ, ε, η) ε η ϑ ,398 35, ,414 7, ,745 1, ,871 42, ,782 9, ,587 2, SOME NON EFFECTIVE UNIFORM ASYMPTOTIC RESULTS Cosider the problem as i the previous Sectio As a o effective asymptotic theorem we have the followig Corollary (Zieliński 1998): if F is a cotiuous ad strictly icreasig distributio fuctio ad k()/ q the X k(): x q as uiformly i the family of distributios F θ (x) = F (x θ), < θ < 10

11 Two more geeral theorems cocerig the covergece of s (θ) = a(x i, θ) where a(x, θ) = ( a 1 (X, θ),, a m (X, θ) ) is a give vector-valued fuctio, are take from Borovkov (1998) To state the theorems recall that a itegral ψ(x, θ)p θ (dx) is said to be coverget i Θ uiformly with respect to θ if sup ψ(x, θ) P θ (dx) 0, as N θ Θ ψ(x,θ) >N theorem 1 (uiform law of large umbers) coverges uiformly i θ Θ, the P θ s (θ) a(θ) > ε 0 If a(θ) = a(x, θ)p θ (dx) as uiformly i θ To state the cetral limit theorem assume that a(θ) = 0 (or take a (X, θ) = a(x, θ) a(θ) istead of a(x, θ)) theorem 2 (uiform cetral limit theorem) If a 2 j (x, θ)p θ(dx), j = 1,, m, coverge uiformly i θ, the s (θ)/ ( ) coverges to a ormal radom variable N 0, σ 2 (θ) uiformly with respect to θ, where σ 2 (θ) = E θ ( a T (X, θ)a(x, θ) ) 5 COMMENTS Though of great importace for statistical iferece, the literature o uiform asymptotic theorems i statistical models, ad especially o effective limit laws, is extremely scarce Perhaps the oly two examples of specific theorems for statistical models are the above result o sample quatiles ad a geeral result o uiform cosistecy of maximum likelihood estimators (Borovkow 1998, Ibragimov et al 1981) Other uiform versios of asymptotic theorems are mostly costructed as follows: take a probability asymptotic theorem which states that if a distributio uder 11

12 cosideratio satisfies a coditio C the W LLN (or SLLN, or CLT ) holds The formulate the statistical theorem: if the coditio C is satisfied uiformly i a give statistical model the W LLN (or, respectively, SLLN, or CLT ) holds uiformly (Ibragimov et al 1981) If a distributio-free statistic i a model uder cosideratio is available, the problem of uiform limit laws is automatically solved, but costructig a effective limit law may be difficult As a example cosider the Kolmogov statistic D = sup x F (x) F (x) i a statistical model with F cotiuous; here F (x) is the empirical distributio fuctio It is well kow that the distributio of D does ot deped o the specific distributio F so that the stochastic covergece P D > ε 0 for every ε > 0 holds uiformly That meas that for every ε > 0 ad for every η > 0 there exists N = N(ε, η) such that for all F cotiuous ad for all > N, P D > ε < η I Birbaum (1952) oe reads that N(015, 01) = 65 ad N(005, 001) = 1, 060 The values were obtaied umerically ad o explicite formula for N(ε, η) is kow 12

13 REFERENCES Birbaum, ZW (1952): Numerical tabulatio of the distributio of Kolmogorov s statistic for fiite sample size, JASA 47, Boratyńska, A ad Zieliński, R (1997): Asymptotic behavior of sample media i a parametric model, Aales UMCS, Sectio A, Vol LI1,2, Borovkov, AA (1998): Mathematical statistics, Gordo ad Breach Brow, LD, Cai, TT ad DasGupta, A (2001): Iterval estimatio for a Biomial Proportio, Statistical Sciece 16, 2, Ibragimov, IA ad Has miskii (1981): Statistical estimatio Asymptotic theory Spriger Jakubowski, J ad Sztecel, R (2001): Wste,p do teorii prawdopodobieństwa Wyd II Script, Warszawa Serflig, RJ (1980): Approximatio theorems of mathematical statistics Wiley Weso lowski, J (2002): Private commuicatio Zieliński, R (1998): Uiform strog cosistecy of sample quatiles, Statist Probab Lett 37,

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