Limit Theorems for Exchangeable Random Variables via Martingales
|
|
- Abner Ramsey
- 5 years ago
- Views:
Transcription
1 Limit Theorems for Exchangeable Random Variables via Martingales Neville Weber, University of Sydney. May 15, 2006 Probabilistic Symmetries and Their Applications
2 A sequence of random variables {X 1, X 2,.., X N } is said to be exchangeable if {X i } D = {X π(i) } for every permutation π of {1, 2,.., N}. An infinite sequence is said to be exchangeable if every finite subset is exchangeable. For an infinite exchangeable sequence if we condition on an appropriate σ-field then the sequence behaves like a sequence of independent and identically distributed random variables (de Finetti (1930)). Blum et al. (1958), Bühlmann (1958) used this result to obtain the central limit theorem for sums of exchangeable random variables. For an array of random variables where each row consists of finitely exchangeable variables the above technique does not apply. Martingale methods provide a unified approach to both situations. The martingale (or reversed martingale) idea was suggested by Loynes (1969) in the context of U-statistics and developed by Eagleson (1979, 1982), Eagleson and Weber (1978), and Weber (1980) for exchangeable variables. Probabilistic Symmetries and Their Applications 1
3 Martingales Let F = {F n } be an increasing sequence of σ-fields, i.e. F n F n+1 and let S 1, S 2,.. be a sequence of random variables such that S i is F i -measurable. F n can be thought of as the history of the process up to time n. {S n } is a martingale with respect to F if E S n <, and E(S n F m ) = S m for all m < n. Martingales include sums of zero mean, i.i.d. random variables, S n = Σ n i=1 X i, and many of the properties of sums carry across to martingales under suitable conditions. Flexibility in the choice of F n makes martingale methods very useful. We will show that we can exploit the symmetry of various exchangeable situations to construct σ-fields that allow us to form martingales and to evaluate key conditional expectations. Probabilistic Symmetries and Their Applications 2
4 Let {Y nj } be a triangular array of martingale differences with respect to {F nj }. Theorem 1. [Scott (1973), Theorem 2] Suppose (i) P n EY 2 nj 1, (ii) max j n Y nj (iii) P n Y 2 nj p 0, and p 1. Then S n = D Y nj N(0, 1). Probabilistic Symmetries and Their Applications 3
5 A Poisson convergence result follows from the results for infinitely divisible laws developed by Brown and Eagleson (1971). See also Freedman (1974). Let {A nj } be an array of events with A nj being F nj -measurable. Theorem 2. If, for some constant λ, (i) P n P (A nj F n,j 1 ) p λ, and (ii) max j n P (A nj F n,j 1 ) p 0, then T n = I(A nj ) D Poisson(λ). Probabilistic Symmetries and Their Applications 4
6 Exchangeable Random Variables Consider an array of row-wise finitely or infinitely exchangeable random variables {X nj : j = 1,.., m n ; n = 1, 2,..}. Construct an array of σ-fields such that X nj is F nj -measurable and F n,j 1 F nj, j = 1, 2,.. Form a martingale difference array and apply Theorem 1 to obtain results for S n = [X nj E(X nj F n,j 1 )]. For exchangeable sequences we can choose F nj so that we can evaluate E(X nj F n,j 1 ). Thus we can obtain conditions under which P n X nj converges in distribution to normal. Specifically, let F n,j 1 = σ{x n1,.., X n,j 1, Xmn i=j X ni } = σ{x n1,.., X n,j 1, Xmn i=1 X ni } Probabilistic Symmetries and Their Applications 5
7 Xmn i=j Xmn X ni = E( X ni F n,j 1 ) = = Xmn i=j Xmn i=j i=j E(X ni F n,j 1 ) E(X nj F n,j 1 ), by exchangeability = (m n j + 1)E(X nj F n,j 1 ). Thus Xmn E(X nj F n,j 1 ) = (m n j + 1) 1 i=j X ni. Probabilistic Symmetries and Their Applications 6
8 Theorem 3. Given {X ni } as above with (i) nex 2 n1 1, (iv) P n X2 nj p 1, (ii) n 2 EX n1 X n2 0, (v) n/m n 0. (iii) p max j n X nj 0, Then S n = D X nj N(0, 1). Proof. Let Y nj = X nj E(X nj F n,j 1 ). EY 2 nj = EX 2 nj E( E 2 (X nj F n,j 1 )). Probabilistic Symmetries and Their Applications 7
9 E( E 2 (X nj F n,j 1 )) = EX 2 n1 /(m n j + 1) + m n j m n j + 1 EX n1x n2 EX 2 n1 log(n + 1) + n EX n1x n2 0, by (i) and (ii). and max j n Y nj max j n X nj + max j n E(X nj F n,j 1 ) max j n E(X nj F n,j 1 ) [ p so (i), (ii) and (iii) imply max j n Y nj 0. Similarly we have P n Y 2 p nj 1. Thus E 2 (X nj F n,j 1 )] 1/2 p 0, D Y nj N(0, 1). Probabilistic Symmetries and Their Applications 8
10 To obtain the final result we need to show that E(X nj F n,j 1 ) p 0. By exchangeability E(X nj F n,j 1 ) = E(X nm n F n,j 1 ) and so for fixed n, {E(X nm n F n,j 1 ), F n,j 1 } is a martingale. Given ɛ > 0 P ( P n E(X nj F n,j 1 ) > ɛ) P (max k n P k E(X nj F n,j 1 ) > ɛ) P (n max k n E(X nm n F nk ) > ɛ) (n/ɛ) 2 E[E(X nm n F nn )] 2, by Kolmogorov s inequality (n/ɛ) 2 [E(X 2 n1 )/(m n n + 1) + EX n1 X n2 ] 0 by (i) and (ii). Probabilistic Symmetries and Their Applications 9
11 Let G nj be the σ-field of measurable sets involving {X n1,.., X nm n} that are unaffected by permuting of the indices 1, 2,.., j, i.e. G nj = σ{(x (1) n1,.., X(j) nj ), X n,j+1,.., X nm n} where (X (1) n1,.., X(j) nj ) denotes the order statistics of the first j terms in the nth row. Here G nj G n,j+1, and, by symmetry, E(X n1 G nj ) = j 1 jx X ni = j 1 S nj. i=1 Thus {j 1 S nj, G nj } is a reversed martingale for each n. Probabilistic Symmetries and Their Applications 10
12 Let Z nj = E(X n1 G nj ) E(X n1 G n,j+1 ) = j 1 {E(X n1 G n,j+1 ) X n,j+1 } so P m n 1 j=n Z nj = E(X n1 G nn ) E(X n1 G nm n) = n 1 P n X nj E(X n1 G nm n). and If we have an exchangeable array with non-random row sums, P mn X nj = 0, say, n 1 X nj = mn 1 X j=n Z nj. Probabilistic Symmetries and Their Applications 11
13 Thus we can represent P n X nj as a sum of reversed martingale differences. The martingale CLT can be applied to the array {Z n,m n j+1, G n,m n j+1} m n n+1 to get the following variation of Chernoff and Teicher (1958). Theorem 4. Let {X ni } be an array of row wise exchangeable random variables with P m n i=1 X ni = 0 for each n. If (i) n/m n α as n, α [0, 1) p (ii) max i m n X ni 0, (iii) P n i=1 X2 p ni 1, as n, then S n D N(0, 1 α). Probabilistic Symmetries and Their Applications 12
14 Similar methods can be applied to arrays of row wise exchangeable events to obtain Poisson limit laws. Let H nj = σ{a n1,.., A nj, P mn i=j+1 I(A ni)} then P (A nj H n,j 1 ) = (m n j + 1) 1 P mn i=j I(A ni). Applying Theorem 2 we obtain Theorem 5. [Ridler-Rowe (1967)] If (i) np (A n1 ) λ, (ii) n 2 P (A n1 A n2 ) λ 2 (iii) n/m n 0 then I(A nj ) D Poisson(λ). Probabilistic Symmetries and Their Applications 13
15 To establish the result note E P (A nj H n,j 1 ) P (A nj ) 2 = E(I(A nj ) Xmn i=j I(A ni )/(m n j + 1) P (A nj ) 2 ) = P (A n1 )/(m n j + 1) + m n j m n j + 1 P (A n1 A n2 ) P (A n1 ) 2 so E P (A nj H n,j 1 ) P (A nj ) n (m n n) 1/2P (A n1) 1/2 + n P (A n1 A n2 ) P (A n1 ) 2 1/2 0, using (i) - (iii). Probabilistic Symmetries and Their Applications 14
16 Contractable Sequences Kallenberg (2000) renewed interest in a more general structure than exchangeability. A sequence of random variables (X 1,..., X n ) taking values in some measurable space (G, G) is contractable (sometimes referred to as spreadable) if for any finite m < n and increasing subsequence of indices k 1 <... < k m, (X k1,..., X k m) = D (X 1,..., X m ). If the above is true for any finite subset of distinct, but not necessarily increasing indices, then the sequence is exchangeable. Contractability implies the variables are identically distributed. Exchangeability and contractability are equivalent in the infinite case (Ryll-Nardzewski (1957)) but not in the finite case. What extra features or conditions are needed for contractable sequences to be exchangeable? Probabilistic Symmetries and Their Applications 15
17 Example (Kallenberg (2000)) (X 1, X 2, X 3 ) takes values in {0, 1, 2}. Joint distribution: Value Probability Value Probability (0,0,1) 2/14 (1,2,2) 1/14 (0,1,2) 2/14 (2,0,0) 2/14 (0,2,0) 2/14 (2,0,2) 1/14 (1,0,2) 1/14 (2,1,0) 1/14 (1,2,0) 1/14 (2,2,1) 1/14 P (X i = 0) = 6/14, P (X i = 1) = 3/14, P (X i = 2) = 5/14. Note (X 1, X 2 ) D = (X 1, X 3 ) D = (X 2, X 3 ), but (X 1, X 2 ) D (X 2, X 1 ) (eg P (X 1 = 2, X 2 = 0) = 3/14 whereas P (X 1 = 0, X 2 = 2) = 2/14). Probabilistic Symmetries and Their Applications 16
18 F - Contractability Given a discrete filtration F = (F 0, F 1,...), the finite or infinite sequence (X 1, X 2,..., ) is F-contractable (exchangeable) if it is adapted to F and when conditioned on F k, θ k X = (X k+1,...) is contractable (exchangeable) for every k. Every contractable sequence is F-contractable wrt the minimal filtration. If X is F-contractable then E(X n F k ) = E(X k+1 F k ), for all n > k. Probabilistic Symmetries and Their Applications 17
19 Specifically if X 1,.., X n are F-contractable where F j 1 = σ{x 1,.., X j 1, X i } = σ{x 1,.., X j 1, i=j X i } i=1 then E(X j F j 1 ) = (n j + 1) 1 X i. This was a key observation in applying martingale weak limit results to obtain results for exchangeable sequences. Lemma 1. [Theorem 1.13, Kallenberg (2005)] If X = (X 1, X 2,.., X n ) is F-contractable and Σ n i=1 X i is F 0 -measurable then X is exchangeable given F 0. i=j Probabilistic Symmetries and Their Applications 18
20 Partially Exchangeable Arrays. U-statistics Given a sequence of iid random variables ξ 1, ξ 2,.. and a function h : R m R which is symmetric in its arguments, the U-statistic with kernel h (of degree m) is U n = n 1X h(ξi1,.., ξ i m), m n where P n denotes summation over 1 i 1 < i 2 <.. < i m n. Examples: 1. Sample moments h(x) = x k 2. Sample variance h(x, y) = 1 2 (x y)2 3. Wilcoxon statistic h(x, y) = I(x + y > 0) 4. Rayleigh statistic h(x, y) = 2 cos(x y), x [0, 2π) Probabilistic Symmetries and Their Applications 19
21 Let I n = σ{(ξ (1),.., ξ (n) ), ξ n+1, ξ n+2,..}. Then I n I n+1 and U n = E(h(ξ 1,.., ξ m ) I n ). Thus {U n } is a reversed martingale wrt {I n }. If E h(ξ 1,.., ξ m ) < strong consistency follows from the reversed mg convergence theorem. Loynes (1969) conjectured that the CLT for U-statistics, established by Hoeffding (1948), should follow via martingale techniques. Probabilistic Symmetries and Their Applications 20
22 Jointly Exchangeable Arrays U-statistics are special cases of partial sums of jointly exchangeable random variables. The array {X i1,i 2,..,im} is jointly (or weakly) exchangeable if {X i1,i 2,..,im} D = {X π(i1 ),π(i 2 ),..,π(im)} (1) where π is a permutation of the positive integers that leaves all but a finite number of terms fixed. Trivially X i1,i 2,..,im = h(ξ i1, ξ i2,.., ξ i m) satisfies (3). The arrays {h(ξ i1,.., ξ i m)} are infact dissociated arrays in that terms with disjoint indexing sets are independent. Limit theorems for dissociated arrays were established by Silverman (1976). Probabilistic Symmetries and Their Applications 21
23 For the infinite, symmetric, jointly exchangeable array case Eagleson and Weber (1978) used n 1X I n = σ{t n, T n+1,..} where T n = E(X 1,2,..,m I n ) = Xi1,i m 2,..,im. {T n, I n } is a reversed martingale and martingale methods were used to establish the central limit theorem for T n. n k (k, k) (0,0) k Figure 1: σ-field structure for m = 2 Probabilistic Symmetries and Their Applications 22
24 Aldous (1981) extended de Finetti s result to arrays and proved that every infinite, jointly exchangeable array is a mixture of dissociated arrays. Thus the central limit result for partial sums can be deduced from this and Silverman (1976). Aldous representation does not hold for finitely jointly exchangeable arrays. Martingale methods are particularly powerful in these cases. Results obtained for sequences of partial sums of finitely, jointly exchangeable random variables have direct application to U-statistics based on samples drawn without replacement from finite populations. To establish limit theorems we need to select filtrations that enable key conditional expectations to be evaluated. Probabilistic Symmetries and Their Applications 23
25 Eagleson (1979) constructed an appropriate filtration to create forward martingales to establish Poisson limit laws. Theorem 6. [Eagleson (1979), Theorem 3] Let {A (n) ij : 1 i, j m n } be a sequence of symmetric, jointly exchangeable events. If, for some constant λ, (i) `n (n) 2 P (A 12 ) λ, (ii) n 3 P (A (n) 12 A(n) 13 ) 0, (iii) `n 2P (n) 2 (A 12 A(n) 34 ) λ2, (iv) n/m n 0, then X 1 i<j n I(A (n) ij ) D Poisson(λ). Probabilistic Symmetries and Their Applications 24
26 k A (n) ij (k, k) (0,0) k Figure 2: σ-field structure The σ-field structure used in this case was F nk = σ{a (n) ij m n, 1 i, j k; U (n) 1k,.., U (n) kk } where U (n) αk = P mn β=k+1 I(A(n) αβ ), α k. F n,k 1 F nk. For i < j P (A (n) ij F n,j 1) = U (n) i,j 1 /(m n j + 1). Probabilistic Symmetries and Their Applications 25
27 Symmetric statistics of U-type By considering sequences of arrays we can obtain results for generalizations of U- statistics where the kernels depend on the sample size. Such statistics arise naturally in analysing spatial data. Given a sequence of iid random variables ξ 1, ξ 2,.. define S n = n 1X hn (ξ i1,.., ξ i m). m n Asymptotic normality can be established via reversed martingale methods using the σ-fields F nj = σ{(ξ (1),.., ξ (j) ), ξ j+1, ξ j+2,..} (Weber (1983)). Poisson limits were obtained by Silverman and Brown (1978). Probabilistic Symmetries and Their Applications 26
28 Examples Interpoint distance statistic - test for clusters h n (ξ 1, ξ 2 ) = I(d(ξ 1, ξ 2 ) βn α ) for some constants α and β, ξ i R k. A normal limit exists for if α [0, 2/k) and a Poisson limit if α = 2/k. Test for collinearity. Kendall and Kendall (1980) suggest using an indicator function h n and counting the number of triples where the largest interior angle is greater than π βn α. If 0 α < 2 then a normal limit exists. Probabilistic Symmetries and Their Applications 27
29 Separately Exchangeable Arrays {X (n) ij : 1 i c n, 1 j r n } D = {X (n) πn(i),σn(j) } where σ n is any permutation of (1, 2,.., c n ) and π n is any permutation of (1, 2,.., r n ). In these cases the arrays are rectangular and so typically when constructing the σ-fields we have to consider row sums, column sums plus the total of the upper rectangular region. Exchangeability within rows and within columns is exploited to evaluate the conditional expectations. Probabilistic Symmetries and Their Applications 28
30 r n j (i, j) (0,0) i c n Figure 3: σ-field structure for separately exchangeable arrays. Probabilistic Symmetries and Their Applications 29
31 This approach can also be used to construct relevant 1- and 2- martingales to obtain 2-d limits. For example, Theorem 7. [Brown et al. (1986), Theorem 4.1] Let {A (n) ij : 1 i r n, 1 j m n } be a sequence of separately exchangeable events. If, for some constant λ, (i) n 2 P (A (n) 11 ) λ, (ii) n 3 P (A (n) 11 A(n) 12 ) 0, (iii) n 3 P (A (n) 11 A(n) 21 ) 0, (iv) n 4 P (A (n) 11 A(n) 22 ) λ2, (v) n/m n 0, and n/r n 0 then [ns] X i=1 [nt] X I(A (n) ij ) converges in distribution to a Poisson process with intensity λ times Lebesgue measure. Probabilistic Symmetries and Their Applications 30
32 References [1] Aldous, D.J. (1981) Representations for partially exchangeable arrays of random variables. J. Multivariate Anal., 11, [2] Blum, J.R., Chernoff, H., Rosenblatt, M. and Teicher, H. (1958). Central limit theorems for interchangeable processes, Canad. J. Math., 10, [3] Brown, B.M. and Eagleson, G.K. (1971) Martingale convergence to infinitely divisible laws with finite variances. Trans. Amer. Math. Soc., 162, [4] Brown, T.C., Ivanoff, B.G. and Weber, N.C. (1986) Poisson convergence in two dimensions with application to row and column exchangeable arrays. Stoch. Proc. Appl., 23, [5] Bühlmann, H. (1958) Le problème limit central pour les variables alèatoires échongeables. C.R. Acad. Sci. Paris, 246, [6] Chernoff, H. and Teicher, H. (1958) A central limit theorem for sums of interchangeable random variables. Ann. Math. Statist., 29, [7] De Finetti, B. (1930) Funzione caratteristica di un fenomeno aleatorio. Atti Accad. Naz. Lincei Rend. Cl. Sci. Fis. Mat. Nat., 4, [8] Eagleson, G.K. (1979) A Poisson limit theorem for weakly exchangeable events. J. Appl. Probab., 16, [9] Eagleson, G.K. (1982) Weak limit theorems for exchangeable random variables. In Exchangeability in Probability and Statistics Ed. G. Koch and F.Spizzichino, North-Holland, Amsterdam-New York, [10] Eagleson, G.K. and Weber, N.C. (1978). Limit theorems for weakly exchangeable arrays. Math. Proc. Camb. Philos. Soc., 84, [11] Freedman, D. (1974) The Poisson approximation for dependent events. Ann. Probab., 2, [12] Hall, P. and Heyde, C.C (1980) Martingale Limit Theory and Its Application. Academic Press, London [13] Hoeffding, W. (1948) A class of statistics with asymptotically normal distribution. Ann. Math. Statist., 19, Probabilistic Symmetries and Their Applications 31
33 [14] Kallenberg, O. (1988) Spreading and predictable sampling in exchangeable sequences and processes. Ann. Probab., 16, [15] Kallenberg, O. (2000) Spreading-invariant sequences and processes on bounded index sets. Probab. Theory Rel. Fields, 118, [16] Kallenberg, O. (2005) Probabilistic Symmetries and Invariance Principles. Springer, New York. [17] Kendall, D.G. and Kendall, W.S. (1980) Alignments in two dimensional random sets of points. Adv. Appl. Probab., 12, [18] Loynes, R.M. (1969) The central limit theorem for backwards martingales. Z. Wahrsch. Verw. Gebiete, 13, 1-8. [19] Ridler-Rowe, C.J. (1967) On two problems on exchangeable events. Studia Sci. Math. Hungar., 2, [20] Ryll-Nardzewski, C. (1957) On stationary sequences of random variables and the de Finetti s equivalence. Colloq. Math., 4, [21] Scott, D.J. (1973) Central limit theorems for martingales and for processes with stationary increments using a Skorokhod representation approach. Adv. Appl. Probab., 5, [22] Silverman, B.W. (1976) Limit theorems for dissociated random variables. Adv. Appl. Probab., 8, [23] Silverman, B.W. and Brown, T.C. (1978) Short distances, flat triangles, and Poisson limits. J. Appl. Probab., 15, [24] Weber, N.C. (1980). A martingale approach to central limit theorems for exchangeable random variables. J. Appl. Probab., 17, [25] Weber, N.C. (1983). Central limit theorems for a class of symmetric statistics. Math. Proc. Camb. Philos. Soc., 94, Probabilistic Symmetries and Their Applications 32
A CLT FOR MULTI-DIMENSIONAL MARTINGALE DIFFERENCES IN A LEXICOGRAPHIC ORDER GUY COHEN. Dedicated to the memory of Mikhail Gordin
A CLT FOR MULTI-DIMENSIONAL MARTINGALE DIFFERENCES IN A LEXICOGRAPHIC ORDER GUY COHEN Dedicated to the memory of Mikhail Gordin Abstract. We prove a central limit theorem for a square-integrable ergodic
More informationHan-Ying Liang, Dong-Xia Zhang, and Jong-Il Baek
J. Korean Math. Soc. 41 (2004), No. 5, pp. 883 894 CONVERGENCE OF WEIGHTED SUMS FOR DEPENDENT RANDOM VARIABLES Han-Ying Liang, Dong-Xia Zhang, and Jong-Il Baek Abstract. We discuss in this paper the strong
More informationAlmost sure limit theorems for U-statistics
Almost sure limit theorems for U-statistics Hajo Holzmann, Susanne Koch and Alesey Min 3 Institut für Mathematische Stochasti Georg-August-Universität Göttingen Maschmühlenweg 8 0 37073 Göttingen Germany
More informationMi-Hwa Ko. t=1 Z t is true. j=0
Commun. Korean Math. Soc. 21 (2006), No. 4, pp. 779 786 FUNCTIONAL CENTRAL LIMIT THEOREMS FOR MULTIVARIATE LINEAR PROCESSES GENERATED BY DEPENDENT RANDOM VECTORS Mi-Hwa Ko Abstract. Let X t be an m-dimensional
More informationSome highlights from the theory of multivariate symmetries
Rendiconti di Matematica, Serie VII Volume 28, Roma (2008), 19 32 Some highlights from the theory of multivariate symmetries OLAV KALLENBERG Abstract: We explain how invariance in distribution under separate
More informationRandom Bernstein-Markov factors
Random Bernstein-Markov factors Igor Pritsker and Koushik Ramachandran October 20, 208 Abstract For a polynomial P n of degree n, Bernstein s inequality states that P n n P n for all L p norms on the unit
More informationA central limit theorem for randomly indexed m-dependent random variables
Filomat 26:4 (2012), 71 717 DOI 10.2298/FIL120471S ublished by Faculty of Sciences and Mathematics, University of Niš, Serbia Available at: http://www.pmf.ni.ac.rs/filomat A central limit theorem for randomly
More informationTsung-Lin Cheng and Yuan-Shih Chow. Taipei115,Taiwan R.O.C.
A Generalization and Alication of McLeish's Central Limit Theorem by Tsung-Lin Cheng and Yuan-Shih Chow Institute of Statistical Science Academia Sinica Taiei5,Taiwan R.O.C. hcho3@stat.sinica.edu.tw Abstract.
More informationExchangeability. Peter Orbanz. Columbia University
Exchangeability Peter Orbanz Columbia University PARAMETERS AND PATTERNS Parameters P(X θ) = Probability[data pattern] 3 2 1 0 1 2 3 5 0 5 Inference idea data = underlying pattern + independent noise Peter
More informationLARGE DEVIATION PROBABILITIES FOR SUMS OF HEAVY-TAILED DEPENDENT RANDOM VECTORS*
LARGE EVIATION PROBABILITIES FOR SUMS OF HEAVY-TAILE EPENENT RANOM VECTORS* Adam Jakubowski Alexander V. Nagaev Alexander Zaigraev Nicholas Copernicus University Faculty of Mathematics and Computer Science
More informationBALANCING GAUSSIAN VECTORS. 1. Introduction
BALANCING GAUSSIAN VECTORS KEVIN P. COSTELLO Abstract. Let x 1,... x n be independent normally distributed vectors on R d. We determine the distribution function of the minimum norm of the 2 n vectors
More informationG(t) := i. G(t) = 1 + e λut (1) u=2
Note: a conjectured compactification of some finite reversible MCs There are two established theories which concern different aspects of the behavior of finite state Markov chains as the size of the state
More informationThe Essential Equivalence of Pairwise and Mutual Conditional Independence
The Essential Equivalence of Pairwise and Mutual Conditional Independence Peter J. Hammond and Yeneng Sun Probability Theory and Related Fields, forthcoming Abstract For a large collection of random variables,
More informationWald for non-stopping times: The rewards of impatient prophets
Electron. Commun. Probab. 19 (2014), no. 78, 1 9. DOI: 10.1214/ECP.v19-3609 ISSN: 1083-589X ELECTRONIC COMMUNICATIONS in PROBABILITY Wald for non-stopping times: The rewards of impatient prophets Alexander
More informationON COMPOUND POISSON POPULATION MODELS
ON COMPOUND POISSON POPULATION MODELS Martin Möhle, University of Tübingen (joint work with Thierry Huillet, Université de Cergy-Pontoise) Workshop on Probability, Population Genetics and Evolution Centre
More informationA NOTE ON THE COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF B-VALUED RANDOM VARIABLES
Bull. Korean Math. Soc. 52 (205), No. 3, pp. 825 836 http://dx.doi.org/0.434/bkms.205.52.3.825 A NOTE ON THE COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF B-VALUED RANDOM VARIABLES Yongfeng Wu and Mingzhu
More informationProbability and Measure
Chapter 4 Probability and Measure 4.1 Introduction In this chapter we will examine probability theory from the measure theoretic perspective. The realisation that measure theory is the foundation of probability
More informationA Martingale Central Limit Theorem
A Martingale Central Limit Theorem Sunder Sethuraman We present a proof of a martingale central limit theorem (Theorem 2) due to McLeish (974). Then, an application to Markov chains is given. Lemma. For
More informationAsymptotic Normality under Two-Phase Sampling Designs
Asymptotic Normality under Two-Phase Sampling Designs Jiahua Chen and J. N. K. Rao University of Waterloo and University of Carleton Abstract Large sample properties of statistical inferences in the context
More informationBounds for pairs in partitions of graphs
Bounds for pairs in partitions of graphs Jie Ma Xingxing Yu School of Mathematics Georgia Institute of Technology Atlanta, GA 30332-0160, USA Abstract In this paper we study the following problem of Bollobás
More informationIndependence of some multiple Poisson stochastic integrals with variable-sign kernels
Independence of some multiple Poisson stochastic integrals with variable-sign kernels Nicolas Privault Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological
More informationOn large deviations for combinatorial sums
arxiv:1901.0444v1 [math.pr] 14 Jan 019 On large deviations for combinatorial sums Andrei N. Frolov Dept. of Mathematics and Mechanics St. Petersburg State University St. Petersburg, Russia E-mail address:
More informationComplete moment convergence of weighted sums for processes under asymptotically almost negatively associated assumptions
Proc. Indian Acad. Sci. Math. Sci.) Vol. 124, No. 2, May 214, pp. 267 279. c Indian Academy of Sciences Complete moment convergence of weighted sums for processes under asymptotically almost negatively
More information5 Birkhoff s Ergodic Theorem
5 Birkhoff s Ergodic Theorem Birkhoff s Ergodic Theorem extends the validity of Kolmogorov s strong law to the class of stationary sequences of random variables. Stationary sequences occur naturally even
More informationAsymptotic Statistics-III. Changliang Zou
Asymptotic Statistics-III Changliang Zou The multivariate central limit theorem Theorem (Multivariate CLT for iid case) Let X i be iid random p-vectors with mean µ and and covariance matrix Σ. Then n (
More informationON THE COMPLETE CONVERGENCE FOR WEIGHTED SUMS OF DEPENDENT RANDOM VARIABLES UNDER CONDITION OF WEIGHTED INTEGRABILITY
J. Korean Math. Soc. 45 (2008), No. 4, pp. 1101 1111 ON THE COMPLETE CONVERGENCE FOR WEIGHTED SUMS OF DEPENDENT RANDOM VARIABLES UNDER CONDITION OF WEIGHTED INTEGRABILITY Jong-Il Baek, Mi-Hwa Ko, and Tae-Sung
More informationStatistical Inference on Large Contingency Tables: Convergence, Testability, Stability. COMPSTAT 2010 Paris, August 23, 2010
Statistical Inference on Large Contingency Tables: Convergence, Testability, Stability Marianna Bolla Institute of Mathematics Budapest University of Technology and Economics marib@math.bme.hu COMPSTAT
More informationPlan 1 Motivation & Terminology 2 Noncommutative De Finetti Theorems 3 Braidability 4 Quantum Exchangeability 5 References
www.math.uiuc.edu/ koestler University of Illinois at Urbana-Champaign / St. Lawrence University GPOTS 2008 University of Cincinnati June 18, 2008 [In parts joint work with Rolf Gohm and Roland Speicher]
More informationA SKOROHOD REPRESENTATION AND AN INVARIANCE PRINCIPLE FOR SUMS OF WEIGHTED i.i.d. RANDOM VARIABLES
ROCKY MOUNTAIN JOURNA OF MATHEMATICS Volume 22, Number 1, Winter 1992 A SKOROHOD REPRESENTATION AND AN INVARIANCE PRINCIPE FOR SUMS OF WEIGHTED i.i.d. RANDOM VARIABES EVAN FISHER ABSTRACT. A Skorohod representation
More informationPRECISE ASYMPTOTIC IN THE LAW OF THE ITERATED LOGARITHM AND COMPLETE CONVERGENCE FOR U-STATISTICS
PRECISE ASYMPTOTIC IN THE LAW OF THE ITERATED LOGARITHM AND COMPLETE CONVERGENCE FOR U-STATISTICS REZA HASHEMI and MOLUD ABDOLAHI Department of Statistics, Faculty of Science, Razi University, 67149, Kermanshah,
More informationCentral Limit Theorem for Non-stationary Markov Chains
Central Limit Theorem for Non-stationary Markov Chains Magda Peligrad University of Cincinnati April 2011 (Institute) April 2011 1 / 30 Plan of talk Markov processes with Nonhomogeneous transition probabilities
More information1. A remark to the law of the iterated logarithm. Studia Sci. Math. Hung. 7 (1972)
1 PUBLICATION LIST OF ISTVÁN BERKES 1. A remark to the law of the iterated logarithm. Studia Sci. Math. Hung. 7 (1972) 189-197. 2. Functional limit theorems for lacunary trigonometric and Walsh series.
More informationAn Asymptotic Property of Schachermayer s Space under Renorming
Journal of Mathematical Analysis and Applications 50, 670 680 000) doi:10.1006/jmaa.000.7104, available online at http://www.idealibrary.com on An Asymptotic Property of Schachermayer s Space under Renorming
More informationMartingale approximations for random fields
Electron. Commun. Probab. 23 (208), no. 28, 9. https://doi.org/0.24/8-ecp28 ISSN: 083-589X ELECTRONIC COMMUNICATIONS in PROBABILITY Martingale approximations for random fields Peligrad Magda * Na Zhang
More informationarxiv: v1 [math.pr] 9 Apr 2015 Dalibor Volný Laboratoire de Mathématiques Raphaël Salem, UMR 6085, Université de Rouen, France
A CENTRAL LIMIT THEOREM FOR FIELDS OF MARTINGALE DIFFERENCES arxiv:1504.02439v1 [math.pr] 9 Apr 2015 Dalibor Volný Laboratoire de Mathématiques Raphaël Salem, UMR 6085, Université de Rouen, France Abstract.
More informationExtreme Value for Discrete Random Variables Applied to Avalanche Counts
Extreme Value for Discrete Random Variables Applied to Avalanche Counts Pascal Alain Sielenou IRSTEA / LSCE / CEN (METEO FRANCE) PostDoc (MOPERA and ECANA projects) Supervised by Nicolas Eckert and Philippe
More informationSet-Indexed Processes with Independent Increments
Set-Indexed Processes with Independent Increments R.M. Balan May 13, 2002 Abstract Set-indexed process with independent increments are described by convolution systems ; the construction of such a process
More informationA note on the growth rate in the Fazekas Klesov general law of large numbers and on the weak law of large numbers for tail series
Publ. Math. Debrecen 73/1-2 2008), 1 10 A note on the growth rate in the Fazekas Klesov general law of large numbers and on the weak law of large numbers for tail series By SOO HAK SUNG Taejon), TIEN-CHUNG
More informationOn the Error Bound in the Normal Approximation for Jack Measures (Joint work with Le Van Thanh)
On the Error Bound in the Normal Approximation for Jack Measures (Joint work with Le Van Thanh) Louis H. Y. Chen National University of Singapore International Colloquium on Stein s Method, Concentration
More informationL n = l n (π n ) = length of a longest increasing subsequence of π n.
Longest increasing subsequences π n : permutation of 1,2,...,n. L n = l n (π n ) = length of a longest increasing subsequence of π n. Example: π n = (π n (1),..., π n (n)) = (7, 2, 8, 1, 3, 4, 10, 6, 9,
More informationAsymptotic efficiency of simple decisions for the compound decision problem
Asymptotic efficiency of simple decisions for the compound decision problem Eitan Greenshtein and Ya acov Ritov Department of Statistical Sciences Duke University Durham, NC 27708-0251, USA e-mail: eitan.greenshtein@gmail.com
More informationP (A G) dp G P (A G)
First homework assignment. Due at 12:15 on 22 September 2016. Homework 1. We roll two dices. X is the result of one of them and Z the sum of the results. Find E [X Z. Homework 2. Let X be a r.v.. Assume
More informationConditional moment representations for dependent random variables
Conditional moment representations for dependent random variables W lodzimierz Bryc Department of Mathematics University of Cincinnati Cincinnati, OH 45 22-0025 bryc@ucbeh.san.uc.edu November 9, 995 Abstract
More informationMOMENT CONVERGENCE RATES OF LIL FOR NEGATIVELY ASSOCIATED SEQUENCES
J. Korean Math. Soc. 47 1, No., pp. 63 75 DOI 1.4134/JKMS.1.47..63 MOMENT CONVERGENCE RATES OF LIL FOR NEGATIVELY ASSOCIATED SEQUENCES Ke-Ang Fu Li-Hua Hu Abstract. Let X n ; n 1 be a strictly stationary
More informationTHE ORDER OF ELEMENTS IN SYLOW p-subgroups OF THE SYMMETRIC GROUP
THE ORDER OF ELEMENTS IN SYLOW p-subgroups OF THE SYMMETRIC GROUP JAN-CHRISTOPH SCHLAGE-PUCHTA Abstract. Define a random variable ξ n by choosing a conjugacy class C of the Sylow p-subgroup of S p n by
More informationWLLN for arrays of nonnegative random variables
WLLN for arrays of nonnegative random variables Stefan Ankirchner Thomas Kruse Mikhail Urusov November 8, 26 We provide a weak law of large numbers for arrays of nonnegative and pairwise negatively associated
More informationGoodness-of-Fit Tests for Time Series Models: A Score-Marked Empirical Process Approach
Goodness-of-Fit Tests for Time Series Models: A Score-Marked Empirical Process Approach By Shiqing Ling Department of Mathematics Hong Kong University of Science and Technology Let {y t : t = 0, ±1, ±2,
More informationExchangeable random arrays
Exchangeable random arrays Tim Austin Notes for IAS workshop, June 2012 Abstract Recommended reading: [Ald85, Aus08, DJ07, Ald10]. Of these, [Aus08] and [DJ07] give special emphasis to the connection with
More informationBahadur representations for bootstrap quantiles 1
Bahadur representations for bootstrap quantiles 1 Yijun Zuo Department of Statistics and Probability, Michigan State University East Lansing, MI 48824, USA zuo@msu.edu 1 Research partially supported by
More informationDAVID ELLIS AND BHARGAV NARAYANAN
ON SYMMETRIC 3-WISE INTERSECTING FAMILIES DAVID ELLIS AND BHARGAV NARAYANAN Abstract. A family of sets is said to be symmetric if its automorphism group is transitive, and 3-wise intersecting if any three
More informationarxiv: v2 [math.nt] 13 Jul 2018
arxiv:176.738v2 [math.nt] 13 Jul 218 Alexander Kalmynin Intervals between numbers that are sums of two squares Abstract. In this paper, we improve the moment estimates for the gaps between numbers that
More informationMean convergence theorems and weak laws of large numbers for weighted sums of random variables under a condition of weighted integrability
J. Math. Anal. Appl. 305 2005) 644 658 www.elsevier.com/locate/jmaa Mean convergence theorems and weak laws of large numbers for weighted sums of random variables under a condition of weighted integrability
More informationCitation Osaka Journal of Mathematics. 41(4)
TitleA non quasi-invariance of the Brown Authors Sadasue, Gaku Citation Osaka Journal of Mathematics. 414 Issue 4-1 Date Text Version publisher URL http://hdl.handle.net/1194/1174 DOI Rights Osaka University
More informationLogarithmic scaling of planar random walk s local times
Logarithmic scaling of planar random walk s local times Péter Nándori * and Zeyu Shen ** * Department of Mathematics, University of Maryland ** Courant Institute, New York University October 9, 2015 Abstract
More informationThe Distribution of Partially Exchangeable Random Variables
The Distribution of Partially Exchangeable Random Variables Hanxiang Peng a,1,, Xin Dang b,1, Xueqin Wang c,2 a Department of Mathematical Sciences, Indiana University-Purdue University at Indianapolis,
More information16. M. Maejima, Some sojourn time problems for strongly dependent Gaussian processes, Z. Wahrscheinlichkeitstheorie verw. Gebiete 57 (1981), 1 14.
Publications 1. M. Maejima, Some limit theorems for renewal processes with non-identically distributed random variables, Keio Engrg. Rep. 24 (1971), 67 83. 2. M. Maejima, A generalization of Blackwell
More informationLimiting behaviour of moving average processes under ρ-mixing assumption
Note di Matematica ISSN 1123-2536, e-issn 1590-0932 Note Mat. 30 (2010) no. 1, 17 23. doi:10.1285/i15900932v30n1p17 Limiting behaviour of moving average processes under ρ-mixing assumption Pingyan Chen
More informationSMSTC (2007/08) Probability.
SMSTC (27/8) Probability www.smstc.ac.uk Contents 12 Markov chains in continuous time 12 1 12.1 Markov property and the Kolmogorov equations.................... 12 2 12.1.1 Finite state space.................................
More informationSTOCHASTIC GEOMETRY BIOIMAGING
CENTRE FOR STOCHASTIC GEOMETRY AND ADVANCED BIOIMAGING 2018 www.csgb.dk RESEARCH REPORT Anders Rønn-Nielsen and Eva B. Vedel Jensen Central limit theorem for mean and variogram estimators in Lévy based
More informationThe strong law of large numbers for arrays of NA random variables
International Mathematical Forum,, 2006, no. 2, 83-9 The strong law of large numbers for arrays of NA random variables K. J. Lee, H. Y. Seo, S. Y. Kim Division of Mathematics and Informational Statistics,
More informationMarcinkiewicz-Zygmund Type Law of Large Numbers for Double Arrays of Random Elements in Banach Spaces
ISSN 995-0802, Lobachevskii Journal of Mathematics, 2009, Vol. 30, No. 4,. 337 346. c Pleiades Publishing, Ltd., 2009. Marcinkiewicz-Zygmund Tye Law of Large Numbers for Double Arrays of Random Elements
More informationEXISTENCE OF MOMENTS IN THE HSU ROBBINS ERDŐS THEOREM
Annales Univ. Sci. Budapest., Sect. Comp. 39 (2013) 271 278 EXISTENCE OF MOMENTS IN THE HSU ROBBINS ERDŐS THEOREM O.I. Klesov (Kyiv, Ukraine) U. Stadtmüller (Ulm, Germany) Dedicated to the 70th anniversary
More informationLecture 2. We now introduce some fundamental tools in martingale theory, which are useful in controlling the fluctuation of martingales.
Lecture 2 1 Martingales We now introduce some fundamental tools in martingale theory, which are useful in controlling the fluctuation of martingales. 1.1 Doob s inequality We have the following maximal
More informationAsymptotics of Integrals of. Hermite Polynomials
Applied Mathematical Sciences, Vol. 4, 010, no. 61, 04-056 Asymptotics of Integrals of Hermite Polynomials R. B. Paris Division of Complex Systems University of Abertay Dundee Dundee DD1 1HG, UK R.Paris@abertay.ac.uk
More informationSpatial autoregression model:strong consistency
Statistics & Probability Letters 65 (2003 71 77 Spatial autoregression model:strong consistency B.B. Bhattacharyya a, J.-J. Ren b, G.D. Richardson b;, J. Zhang b a Department of Statistics, North Carolina
More informationAn almost sure central limit theorem for the weight function sequences of NA random variables
Proc. ndian Acad. Sci. (Math. Sci.) Vol. 2, No. 3, August 20, pp. 369 377. c ndian Academy of Sciences An almost sure central it theorem for the weight function sequences of NA rom variables QUNYNG WU
More informationOn the asymptotic normality of frequency polygons for strongly mixing spatial processes. Mohamed EL MACHKOURI
On the asymptotic normality of frequency polygons for strongly mixing spatial processes Mohamed EL MACHKOURI Laboratoire de Mathématiques Raphaël Salem UMR CNRS 6085, Université de Rouen France mohamed.elmachkouri@univ-rouen.fr
More informationLectures 16-17: Poisson Approximation. Using Lemma (2.4.3) with θ = 1 and then Lemma (2.4.4), which is valid when max m p n,m 1/2, we have
Lectures 16-17: Poisson Approximation 1. The Law of Rare Events Theorem 2.6.1: For each n 1, let X n,m, 1 m n be a collection of independent random variables with PX n,m = 1 = p n,m and PX n,m = = 1 p
More informationASYMPTOTIC NORMALITY UNDER TWO-PHASE SAMPLING DESIGNS
Statistica Sinica 17(2007), 1047-1064 ASYMPTOTIC NORMALITY UNDER TWO-PHASE SAMPLING DESIGNS Jiahua Chen and J. N. K. Rao University of British Columbia and Carleton University Abstract: Large sample properties
More informationCOMPLETE QTH MOMENT CONVERGENCE OF WEIGHTED SUMS FOR ARRAYS OF ROW-WISE EXTENDED NEGATIVELY DEPENDENT RANDOM VARIABLES
Hacettepe Journal of Mathematics and Statistics Volume 43 2 204, 245 87 COMPLETE QTH MOMENT CONVERGENCE OF WEIGHTED SUMS FOR ARRAYS OF ROW-WISE EXTENDED NEGATIVELY DEPENDENT RANDOM VARIABLES M. L. Guo
More informationA Generalization of Wigner s Law
A Generalization of Wigner s Law Inna Zakharevich June 2, 2005 Abstract We present a generalization of Wigner s semicircle law: we consider a sequence of probability distributions (p, p 2,... ), with mean
More informationConcentration of Measures by Bounded Size Bias Couplings
Concentration of Measures by Bounded Size Bias Couplings Subhankar Ghosh, Larry Goldstein University of Southern California [arxiv:0906.3886] January 10 th, 2013 Concentration of Measure Distributional
More informationOn prediction and density estimation Peter McCullagh University of Chicago December 2004
On prediction and density estimation Peter McCullagh University of Chicago December 2004 Summary Having observed the initial segment of a random sequence, subsequent values may be predicted by calculating
More informationFixed Point Theorem for Cyclic (µ, ψ, φ)-weakly Contractions via a New Function
DOI: 10.1515/awutm-2017-0011 Analele Universităţii de Vest, Timişoara Seria Matematică Informatică LV, 2, 2017), 3 15 Fixed Point Theorem for Cyclic µ, ψ, φ)-weakly Contractions via a New Function Muaadh
More informationfor all f satisfying E[ f(x) ] <.
. Let (Ω, F, P ) be a probability space and D be a sub-σ-algebra of F. An (H, H)-valued random variable X is independent of D if and only if P ({X Γ} D) = P {X Γ}P (D) for all Γ H and D D. Prove that if
More informationWeak-Star Convergence of Convex Sets
Journal of Convex Analysis Volume 13 (2006), No. 3+4, 711 719 Weak-Star Convergence of Convex Sets S. Fitzpatrick A. S. Lewis ORIE, Cornell University, Ithaca, NY 14853, USA aslewis@orie.cornell.edu www.orie.cornell.edu/
More informationOn the Set of Limit Points of Normed Sums of Geometrically Weighted I.I.D. Bounded Random Variables
On the Set of Limit Points of Normed Sums of Geometrically Weighted I.I.D. Bounded Random Variables Deli Li 1, Yongcheng Qi, and Andrew Rosalsky 3 1 Department of Mathematical Sciences, Lakehead University,
More informationPoisson Process Approximation: From Palm Theory to Stein s Method
IMS Lecture Notes Monograph Series c Institute of Mathematical Statistics Poisson Process Approximation: From Palm Theory to Stein s Method Louis H. Y. Chen 1 and Aihua Xia 2 National University of Singapore
More information(1) 0 á per(a) Í f[ U, <=i
ON LOWER BOUNDS FOR PERMANENTS OF (, 1) MATRICES1 HENRYK MINC 1. Introduction. The permanent of an «-square matrix A = (a^) is defined by veria) = cesn n II «"( j. If A is a (, 1) matrix, i.e. a matrix
More informationInjective semigroup-algebras
Injective semigroup-algebras J. J. Green June 5, 2002 Abstract Semigroups S for which the Banach algebra l (S) is injective are investigated and an application to the work of O. Yu. Aristov is described.
More informationStein Couplings for Concentration of Measure
Stein Couplings for Concentration of Measure Jay Bartroff, Subhankar Ghosh, Larry Goldstein and Ümit Işlak University of Southern California [arxiv:0906.3886] [arxiv:1304.5001] [arxiv:1402.6769] Borchard
More informationShih-sen Chang, Yeol Je Cho, and Haiyun Zhou
J. Korean Math. Soc. 38 (2001), No. 6, pp. 1245 1260 DEMI-CLOSED PRINCIPLE AND WEAK CONVERGENCE PROBLEMS FOR ASYMPTOTICALLY NONEXPANSIVE MAPPINGS Shih-sen Chang, Yeol Je Cho, and Haiyun Zhou Abstract.
More informationSoo Hak Sung and Andrei I. Volodin
Bull Korean Math Soc 38 (200), No 4, pp 763 772 ON CONVERGENCE OF SERIES OF INDEENDENT RANDOM VARIABLES Soo Hak Sung and Andrei I Volodin Abstract The rate of convergence for an almost surely convergent
More informationPoisson approximations
Chapter 9 Poisson approximations 9.1 Overview The Binn, p) can be thought of as the distribution of a sum of independent indicator random variables X 1 + + X n, with {X i = 1} denoting a head on the ith
More informationThe Degree of the Splitting Field of a Random Polynomial over a Finite Field
The Degree of the Splitting Field of a Random Polynomial over a Finite Field John D. Dixon and Daniel Panario School of Mathematics and Statistics Carleton University, Ottawa, Canada fjdixon,danielg@math.carleton.ca
More informationChapter 2: Fundamentals of Statistics Lecture 15: Models and statistics
Chapter 2: Fundamentals of Statistics Lecture 15: Models and statistics Data from one or a series of random experiments are collected. Planning experiments and collecting data (not discussed here). Analysis:
More informationarxiv: v2 [math.nt] 21 May 2016
arxiv:165.156v2 [math.nt] 21 May 216 PROGRESSION-FREE SETS IN Z n 4 ARE EXPONENTIALLY SMALL ERNIE CROOT, VSEVOLOD F. LEV, AND PÉTER PÁL PACH Abstract. We show that for integer n 1, any subset A Z n 4 free
More informationMaximizing the descent statistic
Maximizing the descent statistic Richard EHRENBORG and Swapneel MAHAJAN Abstract For a subset S, let the descent statistic β(s) be the number of permutations that have descent set S. We study inequalities
More informationAsymptotics for posterior hazards
Asymptotics for posterior hazards Pierpaolo De Blasi University of Turin 10th August 2007, BNR Workshop, Isaac Newton Intitute, Cambridge, UK Joint work with Giovanni Peccati (Université Paris VI) and
More informationSOME IDENTITIES FOR THE RIEMANN ZETA-FUNCTION II. Aleksandar Ivić
FACTA UNIVERSITATIS (NIŠ Ser. Math. Inform. 2 (25, 8 SOME IDENTITIES FOR THE RIEMANN ZETA-FUNCTION II Aleksandar Ivić Abstract. Several identities for the Riemann zeta-function ζ(s are proved. For eample,
More informationBack circulant Latin squares and the inuence of a set. L F Fitina, Jennifer Seberry and Ghulam R Chaudhry. Centre for Computer Security Research
Back circulant Latin squares and the inuence of a set L F Fitina, Jennifer Seberry and Ghulam R Chaudhry Centre for Computer Security Research School of Information Technology and Computer Science University
More informationConcentration of Measures by Bounded Couplings
Concentration of Measures by Bounded Couplings Subhankar Ghosh, Larry Goldstein and Ümit Işlak University of Southern California [arxiv:0906.3886] [arxiv:1304.5001] May 2013 Concentration of Measure Distributional
More informationA regeneration proof of the central limit theorem for uniformly ergodic Markov chains
A regeneration proof of the central limit theorem for uniformly ergodic Markov chains By AJAY JASRA Department of Mathematics, Imperial College London, SW7 2AZ, London, UK and CHAO YANG Department of Mathematics,
More informationarxiv: v1 [math.pr] 17 May 2009
A strong law of large nubers for artingale arrays Yves F. Atchadé arxiv:0905.2761v1 [ath.pr] 17 May 2009 March 2009 Abstract: We prove a artingale triangular array generalization of the Chow-Birnbau- Marshall
More informationA NEW PROOF OF ROTH S THEOREM ON ARITHMETIC PROGRESSIONS
A NEW PROOF OF ROTH S THEOREM ON ARITHMETIC PROGRESSIONS ERNIE CROOT AND OLOF SISASK Abstract. We present a proof of Roth s theorem that follows a slightly different structure to the usual proofs, in that
More informationStochastic comparison of multivariate random sums
Stochastic comparison of multivariate random sums Rafa l Kuli Wroc law University Mathematical Institute Pl. Grunwaldzi 2/4 50-384 Wroc law e-mail: ruli@math.uni.wroc.pl April 13, 2004 Mathematics Subject
More informationAsymptotics for posterior hazards
Asymptotics for posterior hazards Igor Prünster University of Turin, Collegio Carlo Alberto and ICER Joint work with P. Di Biasi and G. Peccati Workshop on Limit Theorems and Applications Paris, 16th January
More informationRandom Process Lecture 1. Fundamentals of Probability
Random Process Lecture 1. Fundamentals of Probability Husheng Li Min Kao Department of Electrical Engineering and Computer Science University of Tennessee, Knoxville Spring, 2016 1/43 Outline 2/43 1 Syllabus
More informationON THE STRONG LIMIT THEOREMS FOR DOUBLE ARRAYS OF BLOCKWISE M-DEPENDENT RANDOM VARIABLES
Available at: http://publications.ictp.it IC/28/89 United Nations Educational, Scientific Cultural Organization International Atomic Energy Agency THE ABDUS SALAM INTERNATIONAL CENTRE FOR THEORETICAL PHYSICS
More informationSupermodular ordering of Poisson arrays
Supermodular ordering of Poisson arrays Bünyamin Kızıldemir Nicolas Privault Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore
More information