A Comparison Theorem on Moment Inequalities Between Negatively Associated and Independent Random Variables

Let {X _i, 1in} be a negatively associated sequence, and let {X* _i , 1in} be a sequence of independent random variables such that X* _i and X _i have the same distribution for each i=1, 2,..., n. It is shown in this paper that Ef( ⁿ _i=1 X _i)Ef( ⁿ _i=1 X* _i ) for any convex function f on R ¹ and that Ef(max_1kn ⁿ _i=k X _i)Ef(max_1kn ^k _i=1 X* _i ) for any increasing convex function. Hence, most of the well-known inequalities, such as the Rosenthal maximal inequality and the Kolmogorov exponential inequality, remain true for negatively associated random variables. In particular, the comparison theorem on moment inequalities between negatively associated and independent random variables extends the Hoeffding inequality on the probability bounds for the sum of a random sample without replacement from a finite population.     

Randomly Weighted Sums of Subexponential Random Variables with Application to Ruin Theory

Let {X _k , 1 k n} be n independent and real-valued random variables with common subexponential distribution function, and let {k, 1 k n} be other n random variables independent of {X _k , 1 k n} and satisfying a _k b for some 0 < a b < for all 1 k n. This paper proves that the asymptotic relations P (max_{1 m n k=1} ^m _k X _k > x) P (sum _k=1 ⁿ _k X _k > x) sum _k=1 ⁿ P ( _k X _k > x) hold as x . In doing so, no any assumption is made on the dependence structure of the sequence { _k , 1 k n}. An application to ruin theory is proposed.     

Which I.I.D. Sums are recurrently dominated by their maximal terms?

For i.i.d. sequences (X _n) we characterize lim sup _n X _n/sup₁i<n|S _i |= in terms of the distribution function. Previous results of Kesten⁽³⁾ and Wittmann⁽⁶⁾ are immediate consequences. We also determine the typical magnitude of sup₁in|S _i |.Partially supported by NSF Grant DMS-9007416     

Remarks on ergodicity of stationary irreducible transient Markov chains

Summary Consider a stationary process {X _n(), – < n < . If the measure of the process is finite (the measure of the whole sample space finite), it is well known that ergodicity of the process {X _n(), - < n < and of each of the subprocesses {X _n(), 0 n < , {X _n(), – < n 0 are equivalent (see [3]). We shall show that this is generally not true for stationary processes with a sigma-finite measure, specifically for stationary irreducible transient Markov chains. An example of a stationary irreducible transient Markov chain {X _n(), - < n <} with {itX_n(), 0 n < < ergodic but {X _n(), < n 0 nonergodic is given. That this can be the case has already been implicitly indicated in the literature [4]. Another example of a stationary irreducible transient Markov chain with both {X _n(), 0 n < and {itX _n(),-< < n 0} ergodic but {X _n(), - < n < nonergodic is presented. In fact, it is shown that all stationary irreducible transient Markov chains {X _n(), - < n < < are nonergodic.This research was supported in part by the Office of Naval Research.John Simon Guggenheim Memorial Fellow.     

Reconstructing dimensions of intersections of convex sets

Denoting by dimA the dimension of the affine hull of the setA, we prove that if {K _i:i T} and {K _i ^j :i T} are two finite families of convex sets inR ⁿ and if dim {K _i :i S} = dim {K _i ^j :i S}for eachS T such that|S| n + 1 then dim {K _i :i T} = dim {K _i : {i T}}.     

Packing random items of three colors

Consider three colors 1,2,3, and forj3, considern items (X _i,j)in of colorj. We want to pack these items inn bins of equal capacity (the bin size is not fixed, and is to be determined once all the objects are known), subject to the condition that each bin must contain exactly one item of each color, and that the total item sizes attributed to any given bin does not exceed the bin capacity. Consider the stochastic model where the random variables (X _i,jj)in,j3 are independent uniformly distributed over [0,1]. We show that there is a polynomial-time algorithm that produces a packing which has a wasted spaceK logn with overwhelming probability.Work partially supported by an N.S.F. grant.     

A strong limit theorem for generalized Cantor-like random sequences

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A Berry–Esseen Bound for U-Statistics in the Non-I.I.D. Case

Let X ₁,..., X _n be independent, not necessarily identically distributed random variables. An optimal Berry–Esseen bound is derived for U-statistics of order 2, that is, statistics of the form T=_1i<jn g _ij(X _i, X _j), where the g _ij are measurable functions such that |g _ij(X _i, X _j)|<. An application is given concerning Wilcoxon's rank-sum test.     

Identification of the Parameters of a Multivariate Normal Vector by the Distribution of the Maximum

This paper continues the work started by Basu and Ghosh (J. Mult. Anal. (1978), 8, 413–429), by Gilliland and Hannan (J. Amer. Stat. Assoc. (1980), 75, No. 371, 651–654), and then continued on by Mukherjea and Stephens (Prob. Theory and Rel. Fields (1990), 84, 289–296), and Elnaggar and Mukherjea (J. Stat. Planning and Inference (1990), 78, 23–37). Let (X₁, X₂,..., X_n) be a multivariate normal vector with zero means, a common correlation and variances ² ₁, ² ₂,..., ² _n such that the parameters , ² ₁, ² ₂,..., s² _n are unknown, but the distribution of the max{X_i: 1in} (or equivalently, the distribution of the min{X_i: 1in}) is known. The problem is whether the parameters are identifiable and then how to determine the (unknown) parameters in terms of the distribution of the maximum (or its density). Here, we solve this problem for general n. Earlier, this problem was considered only for n3. Identifiability problems in related contexts were considered earlier by numerous authors including: T. W. Anderson and S. G. Ghurye, A. A. Tsiatis, H. A. David, S. M. Berman, A. Nadas, and many others. We also consider here the case where the X_i's have a common covariance instead of a common correlation.     

Random Logistic Maps II. The Critical Case

Let (X _n ) ₀ be a Markov chain with state space S=[0,1] generated by the iteration of i.i.d. random logistic maps, i.e., X _n+1=C _n+1 X _n (1–X _n ),n0, where (C _n ) ₁ are i.i.d. random variables with values in [0, 4] and independent of X ₀. In the critical case, i.e., when E(log C ₁)=0, Athreya and Dai⁽²⁾ have shown that X _n ^P 0. In this paper it is shown that if P(C ₁=1)<1 and E(log C ₁)=0 then(i) X _n does not go to zero with probability one (w.p.1) and in fact, there exists a 0<<1 and a countable set (0,1) such that for all xA(0,1), P _x (X _n for infinitely many n1)=1, where P _x stands for the probability distribution of (X _n ) ₀ with X ₀=x w.p.1. A is a closed set for (X _n ) ₀.(ii) If is the supremum of the support of the distribution of C ₁, then for all xA (a)

Triple Positive Solutions for Multipoint Conjugate Boundary Value Problems

For the nth order nonlinear differential equation y ⁽ⁿ⁾(t)=f(y(t)), t [0,1], satisfying the multipoint conjugate boundary conditions, y ^(j)(a_i) = 0,1 i k, 0 j n _i - 1, 0 =a ₁ < a ₂ < < a _k = 1, and _i=1 ^k n _i =n, where f: [0, ) is continuous, growth condtions are imposed on f which yield the existence of at least three solutions that belong to a cone.     

An explicit calculation of the mean of the perimeter of the convex hull of a plane random walk

If (X _n ) _n ⁼¹ is a sequence of i.i.d. random variables in the Euclidean plane such that we compute the mean of the perimeter of theconvex hull ofX ₁++X _k; 0kn}.     

Stochastically monotone Markov Chains

Summary A real-valued discrete time Markov Chain {X _n} is defined to be stochastically monotone when its one-step transition probability function pr {X _n+1y¦ X _n=x} is non-increasing in x for every fixed y. This class of Markov Chains arises in a natural way when it is sought to bound (stochastically speaking) the process {X _n} by means of a smaller or larger process with the same transition probabilities; the class includes many simple models of applied probability theory. Further, a given stochastically monotone Markov Chain can readily be bounded by another chain {Y _n}, with possibly different transition probabilities and not necessarily stochastically monotone, and this is of particular value when the latter process leads to simpler algebraic manipulations. A stationary stochastically monotone Markov Chain {X _n} has cov(f(X ₀), f(X _n)) cov(f(X ₀), f(X _n+1))0 (n =1, 2,...) for any monotonic function f(·). The paper also investigates the definition of stochastic monotonicity on a more general state space, and the properties of integer-valued stochastically monotone Markov Chains.     

An Inequality Involving the Local Eigenvalues of a Distance-Regular Graph

Let denote a distance-regular graph with diameter D 3, valency k, and intersection numbers a _i, b _i, c _i. Let X denote the vertex set of and fix x X. Let denote the vertex-subgraph of induced on the set of vertices in X adjacent X. Observe has k vertices and is regular with valency a ₁. Let _{1 2} ··· _k denote the eigenvalues of and observe ₁ = a ₁. Let denote the set of distinct scalars among ₂, ₃, ..., _k . For let mult denote the number of times appears among ₂, ₃,..., _k . Let denote an indeterminate, and let p ₀, p₁, ...,p _D denote the polynomials in [] satisfying p ₀ = 1 andp _i = c _i+1 p _i+1 + (a _i – c _i+1 + c _i)p _i + b _i p _i–1 (0 i D – 1),where p _–1 = 0. We show where we abbreviate = –1 – b ₁(1+)^–1. Concerning the case of equality we obtain the following result. Let T = T(x) denote the subalgebra of Mat _X ( ) generated by A, E*₀, E*₁, ..., E* _D , where A denotes the adjacency matrix of and E* _i denotes the projection onto the ith subconstituent of with respect to X. T is called the subconstituent algebra or the Terwilliger algebra. An irreducible T-module W is said to be thin whenever dimE* _i W 1 for 0 i D. By the endpoint of W we mean min{i|E* _i W 0}. We show the following are equivalent: (i) Equality holds in the above inequality for 1 i D – 1; (ii) Equality holds in the above inequality for i = D – 1; (iii) Every irreducible T-module with endpoint 1 is thin.     

Weak Laws with Random Indices for Arrays of Random Elements in Rademacher Type p Banach Spaces

For a sequence of constants {a _n,n1}, an array of rowwise independent and stochastically dominated random elements { V _nj, j1, n1} in a real separable Rademacher type p (1p2) Banach space, and a sequence of positive integer-valued random variables {T _n, n1}, a general weak law of large numbers of the form is established where {c _nj, j1, n1}, _n , b _n are suitable sequences. Some related results are also presented. No assumption is made concerning the existence of expected values or absolute moments of the {V _nj, j1, n1}. Illustrative examples include one wherein the strong law of large numbers fails.     

Efficiency and the uniform linear minorization of convex functions

LetX be a Banach space, and let {f _i:iI} be a family of proper lower semicontinuous convex functions defined onX, each of whose epigraphs meets a fixed bound subset ofX×. We say that {f _i:iI} is uniformly linearly minorized if there exists a positive scalar such that for alliI andxX, we havef _i(x)–(1+x). We present two very different characterizations of uniform linear minorization for such a family. Using one of these, we show that either strong or weak epi-convergence of a sequence of convex functions at some point in the effective domain of the limit implies, uniform linear minorization for the entire sequence.With 1 Figure     

Locally most powerful rank tests for testing randomness and symmetry

Let X _i, 1 i N, be N independent random variables (i.r.v.) with distribution functions (d.f.) F _i(x,), 1 i N, respectively, where is a real parameter. Assume furthermore that F _i(·,0) = F(·) for 1 i N. Let R = (R ₁,R _N) and R ⁺,...,R _N ⁺ be the rank vectors of X = (X ₁,X _N) and |X|=(|X ₁|,...,|X _N|), respectively, and let V = (V ₁,V _N) be the sign vector of X. The locally most powerful rank tests (LMPRT) S = S(R) and the locally most powerful signed rank tests (LMPSRT) S = S(R ⁺, V) will be found for testing = 0 against > 0 or < 0 with F being arbitrary and with F symmetric, respectively.     

An Inequality for Tail Probabilities of Martingales with Bounded Differences

Let M _n =X₁+...+X_n be a martingale with bounded differences X_m=M_m-M_m-1 such that {|X_m| _m}=1 with some nonnegative _m. Write ²= ₁ ² + ... + _n ² . We prove the inequalities {M _nx}c(1-(x/)), {M _n x} 1- c(1- (-x/)) with a constant . The result yields sharp inequalities in some models related to the measure concentration phenomena.     

Asymptotics of Dominated Gaussian Maxima

Let {X _n , n1} be a sequence of independent Gaussian random vectors in R ^d d2. In this paper an asymptotic evaluation of P{max₁in X _i a _n Z+b _n } with Z another Gaussian random vector is obtained for a _n, b _n R ^d two vectors obeying certain conditions.