Unimodality of uniform generalized order statistics,with applications to mean bounds

We prove that uniform generalized order statistics are unimodal for an arbitrary choice of model parameters. The result is applied to establish optimal lower and upper bounds on the expectations of generalized order statistics based on nonnegative samples in the population mean unit of measurement. The bounds are attained by two-point distributions.     

Evaluating Improvements for Spacings of Order Statistics

We evaluate sharp upper bounds for the consecutive spacings of order statistics from an i.i.d. sample, measured in scale units generated by various central absolute moments of the parent distribution. The bounds are based on the projection method combined with the Hölder inequalities. We characterize the probability distributions for which the bounds are attained. We also evaluate the so obtained bounds numerically and compare them with other existing bounds.     

On Moments of Bivariate Order Statistics

In the present paper, we give the exact explicit expression for the product moments (of any order) of bivariate order statistics (o.s.) from any arbitrary continuous bivariate distribution function (d.f.). Furthermore, for any arbitrary bivariate uniform d.f., universal distribution-free bounds for the differences of any two different product moments (of order (1,1) or (-1,1)) are given.     

Reliability and expectation bounds for coherent systems with exchangeable components

Sharp upper and lower bounds are obtained for the reliability functions and the expectations of lifetimes of coherent systems based on dependent exchangeable absolutely continuous components with a given marginal distribution function, by use of the concept of Samaniego's signature. We first show that the distribution of any coherent system based on exchangeable components with absolutely continuous joint distribution is a convex combination of distributions of order statistics (equivalent to the k-out-of-n systems) with the weights identical with the values of the Samaniego signature of the system. This extends the Samaniego representation valid for the case of independent and identically distributed components. Combining the representation with optimal bounds on linear combinations of distribution functions of order statistics from dependent identically distributed samples, we derive the corresponding reliability and expectation bounds, dependent on the signature of the system and marginal distribution of dependent components. We also present the sequences of exchangeable absolutely continuous joint distributions of components which attain the bounds in limit. As an application, we obtain the reliability bounds for all the coherent systems with three and four exchangeable components, expressed in terms of the parent marginal reliability function and specify the respective expectation bounds for exchangeable exponential components, comparing them with the lifetime expectations of systems with independent and identically distributed exponential components.     

Two Simple Derivations of Universal Bounds for the C.B.S. Inequality Constant

Universal bounds for the constant in the strengthened Cauchy-Bunyakowski-Schwarz inequality for piecewise linear-linear and piecewise quadratic-linear finite element spaces in 2 space dimensions are derived. The bounds hold for arbitrary shaped triangles, or equivalently, arbitrary matrix coefficients for both the scalar diffusion problems and the elasticity theory equations.     

p-Norm bounds on the expectation of the maximum of a possibly dependent sample

Let X₁, X₂,…, X_n be identically distributed possibly dependent random variables with finite pth absolute moment assumed without loss of generality to be equal to 1. Denote the order statistics by X_1:n, X_2:n,…, X_n:n. Bounds are derived for E(X_n:n) when it is assumed that the X_i's are (i) arbitrarily dependent and (ii) independent. The effect of assuming a symmetric common distribution for the X_i's is discussed. Analogous bounds are described for the expected range of the sample. Bounds on expectations of general linear combinations of order statistics are described in the independent case.     

Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation

Tail index estimation depends for its accuracy on a precise choice of the sample fraction, i.e., the number of extreme order statistics on which the estimation is based. A complete solution to the sample fraction selection is given by means of a two-step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the asymptotic mean-squared error. Unlike previous methods, prior knowledge of the second-order parameter is not required. In addition, we are able to dispense with the need for a prior estimate of the tail index which already converges roughly at the optimal rate. The only arbitrary choice of parameters is the number of Monte Carlo replications.     

Universal Inequalities for Eigenvalues of the Buckling Problem of Arbitrary Order

We investigate the eigenvalues of the buckling problem of arbitrary order on compact domains in Euclidean spaces and spheres. We obtain universal bounds for the kth eigenvalue in terms of the lower eigenvalues independently of the particular geometry of the domain.     

On exceedances of record and order statistics

Exact, limiting distributions along with their rates of convergence of exceedance statistics for both order statistics and record statistics are provided when the underlying distribution is arbitrary. The exact distribution of record statistics for arbitrary underlying distributions is obtained as well.

     

Huang-Yang-Luttinger model: Gaussian dominance and Bose condensation

We prove the Gaussian dominance condition for the Huang-Yang-Luttinger model in the case with an arbitrary chemical potential. Using this condition in the framework of the method of correlation inequalities, we obtain two-sided bounds on the corresponding two-point thermodynamic means. Based on these bounds, we prove the existence of Bose condensation in this model. Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 121, No. 2, pp. 347–352, November, 1999.     

A stochastic analysis of the NFD bin-packing algorithm

We consider the Next-Fit-Decreasing (NFD) algorithm for the one-dimensional bin packing problem. The input consists of n pieces of i.i.d. sizes. Bounds on the first two moments of the number of bins used in the packing are established for arbitrary size distributions. These bounds are asymptotically tight. For the uniform distribution we also exhibit numerical results and examine their dependence on the support of the size distribution.     

Inequalities for the Extremal Coefficients of Multivariate Extreme Value Distributions

The extremal coefficients are the natural dependence measures for multivariate extreme value distributions. For an m-variate distribution 2^m distinct extremal coefficients of different orders exist; they are closely linked and therefore a complete set of 2^m coefficients cannot take any arbitrary values. We give a full characterization of all the sets of extremal coefficients. To this end, we introduce a simple class of extreme value distributions that allows for a 1-1 mapping to the complete sets of extremal coefficients. We construct bounds that higher order extremal coefficients need to satisfy to be consistent with lower order extremal coefficients. These bounds are useful as lower order extremal coefficients are the most easily inferred from data.     

Exact Bounds for the Expectations of Order Statistics from Non-Negative Populations

Some new exact bounds for the expected values of order statistics, under the assumption that the parent population is non-negative, are obtained in terms of the population mean. Similar bounds for the differences of any two order statistics are also given. It is shown that the existing bounds for the general case can be improved considerably under the above assumption.     

最大最小次序统计量的联合分布

X1,X2,…,Xn是来自总体X的简单随机样本,Nk=min1≤i≤k{Xi},Mk=max1≤i≤k{Xi}(k=1,2,…,n),本文给出了最小次序统计量与最大次序统计量的联合分布函数.     

On the robustness of Bayesian networks to learning from non-conjugate sampling

Recent results concerning the instability of Bayes Factor search over Bayesian Networks (BN’s) lead us to ask whether learning the parameters of a selected BN might also depend heavily on the often rather arbitrary choice of prior density. Robustness of inferences to misspecification of the prior density would at least ensure that a selected candidate model would give similar predictions of future data points given somewhat different priors and a given large training data set. In this paper we derive new explicit total variation bounds on the calculated posterior density as the function of the closeness of the genuine prior to the approximating one used and certain summary statistics of the calculated posterior density. We show that the approximating posterior density often converges to the genuine one as the number of sample point increases and our bounds allow us to identify when the posterior approximation might not. To prove our general results we needed to develop a new family of distance measures called local DeRobertis distances. These provide coarse non-parametric neighbourhoods and allowed us to derive elegant explicit posterior bounds in total variation. The bounds can be routinely calculated for BNs even when the sample has systematically missing observations and no conjugate analyses are possible.     

On approximations of transformed chi-squared distributions in statistical applications

We obtain some computable error bounds of order O(n ^?1) for the chi-squared approximation of transformed chi-squared random variables with n degrees of freedom. The results are applied to likelihood ratio statistics in the multivariate case.     

Nodal volumes for eigenfunctions of analytic regular elliptic problems

We establish sharp upper bounds on the (n−1)-dimensional Hausdorff measure of the zero (nodal) sets and on the maximal order of vanishing corresponding to eigenfunctions of a regular elliptic problem on a bounded domain Ω ⊆ ℝ ⁿ with real-analytic boundary. The elliptic operator may be of an arbitrary even order, and its coefficients are assumed to be real-analytic. This extends a result of Donnelly and Fefferman ([DF1], [DF3]) concerning upper bounds for nodal volumes of eigenfunctions corresponding to the Laplacian on compact Riemannian manifolds with boundary.     

Some counting questions for matrices with restricted entries

We obtain upper bounds for the number of arbitrary and symmetric matrices with integer entries in a given box (in an arbitrary location) and a given determinant. We then apply these bounds to estimate the number of matrices in such boxes which have an integer eigenvalues. Finally, we outline some open questions.     

Computing Bounds on the Expected Maximum of Correlated Normal Variables

We compute upper and lower bounds on the expected maximum of correlated normal variables (up to a few hundred in number) with arbitrary means, variances, and correlations. Two types of bounding processes are used: perfectly dependent normal variables, and independent normal variables, both with arbitrary mean values. The expected maximum for the perfectly dependent variables can be evaluated in closed form; for the independent variables, a single numerical integration is required. Higher moments are also available. We use mathematical programming to find parameters for the processes, so they will give bounds on the expected maximum, rather than approximations of unknown accuracy. Our original application is to the maximum number of people on-line simultaneously during the day in an infinite-server queue with a time-varying arrival rate. The upper and lower bounds are tighter than previous bounds, and in many of our examples are within 5% or 10% of each other. We also demonstrate the bounds’ performance on some PERT models, AR/MA time series, Brownian motion, and product-form correlation matrices.     

Bounds for the sample size to justify normal approximation of the confidence level

The normal approximation of the confidence level of the standard confidence intervals leaves an error of the order O(1/n) (and not only O(n ^-1/2)). We use the first order term in the error to obtain simple lower bounds for the sample size.