The simplex dispersion ordering and its application to the evaluation of human corneal endothelia

A multivariate dispersion ordering based on random simplices is proposed in this paper. Given a R^d-valued random vector, we consider two random simplices determined by the convex hulls of two independent random samples of sizes d+1 of the vector. By means of the stochastic comparison of the Hausdorff distances between such simplices, a multivariate dispersion ordering is introduced. Main properties of the new ordering are studied. Relationships with other dispersion orderings are considered, placing emphasis on the univariate version. Some statistical tests for the new order are proposed. An application of such ordering to the clinical evaluation of human corneal endothelia is provided. Different analyses are included using an image database of human corneal endothelia.     

Self-similar random measures

Summary A set is called self-similar if it is decomposable into parts which are similar to the whole. This notion was generalized to random sets. In the present paper an alternative, axiomatic approach is given which makes precise the following idea (using Palm distribution theory): A random set is statistically self-similar if it is statistically scale invariant with respect to any center chosen at random from that set. For these sets Hausdorff dimension coincides with an intrinsic self-similarity index.     

Lower bounds for the complexity of the graph of the Hausdorff distance as a function of transformation

The Hausdorff distance is a measure defined between two sets in some metric space. This paper investigates how the Hausdorff distance changes as one set is transformed by some transformation group. Algorithms to find the minimum distance as one set is transformed have been described, but few lower bounds are known. We consider the complexity of the graph of the Hausdorff distance as a function of transformation, and exhibit some constructions that give lower bounds for this complexity. We exhibit lower-bound constructions for both sets of points in the plane, and sets of points and line segments; we consider the graph of the directed Hausdorff distance under translation, rigid motion, translation and scaling, and affine transformation. Many of the results can also be extended to the undirected Hausdorff distance. These lower bounds are for the complexity of the graph of the Hausdorff distance, and thus do not necessarily bound algorithms that search this graph; however, they do give an indication of how complex the search may be. This work was supported in part by National Science Foundation PYI Grant IRI-9057928 and matching funds from General Electric, Kodak, and Xerox, and in part by Air Force Contract AFOSR-91-0328.     

Polar sets for anisotropic Gaussian random fields

This paper studies polar sets for anisotropic Gaussian random fields, i.e. sets which a Gaussian random field does not hit almost surely. The main assumptions are that the eigenvalues of the covariance matrix are bounded from below and that the canonical metric associated with the Gaussian random field is dominated by an anisotropic metric. We deduce an upper bound for the hitting probabilities and conclude that sets with small Hausdorff dimension are polar. Moreover, the results allow for a translation of the Gaussian random field by a random field, that is independent of the Gaussian random field and whose sample functions are of bounded Hölder norm.     

On Well-Posedness and Hausdorff Convergence of Solution Sets of Vector Optimization Problems

In this paper, we refine and improve the results established in a 2003 paper by Deng in a number of directions. Specifically, we establish a well-posedness result for convex vector optimization problems under a condition which is weaker than that used in the paper. Among other things, we also obtain a characterization of well-posedness in terms of Hausdorff distance of associated sets.     

不可微向量优化问题严有效解的最优性条件

本文研究向量优化问题在严有效解意义下的最优性条件.在局部凸Hausdorff拓扑线性空间中.在近似锥一次类凸假设下,利用凸集分离定理得到了最优性必要条件.借助Gateaux导数引进了几种新的凸性,在新的凸性假设下得到了最优性充分条件.     

Laws of Large Numbers for Exchangeable Random Sets in Kuratowski-Mosco Sense

Abstract

Most of the results for laws of large numbers based on Banach space valued random sets assume that the sets are independent and identically distributed (IID) and compact, in which Rådström embedding or the refined method for collection of compact and convex subsets of a Banach space plays an important role. In this paper, exchangeability among random sets as a dependency, instead of IID, is assumed in obtaining strong laws of large numbers, since some kind of dependency of random variables may be often required for many statistical analyses. Also, the Hausdorff convergence usually used is replaced by another topology, Kuratowski-Mosco convergence. Thus, we prove strong laws of large numbers for exchangeable random sets in Kuratowski-Mosco convergence, without assuming the sets are compact, which is weaker than Hausdorff sense.     

Approximation of Planar Convex Sets from Hyperplane Probes

This paper studies a geometric probing problem. Suppose that an unknown convex set in R ² can be probed by an oracle which, when given a unit vector, will return the position of the supporting hyperplane of the convex set that has the given vector as an outward normal. We present an on-line algorithm for choosing probing directions so that, after n probes, an inner and an outer estimate of the convex set are obtained that are within of each other in Hausdorff distance. This is optimal since there exist convex sets that, even if visible, cannot be approximated better than with n-sided polygons, for example, a circle. Received April 18, 1995, and in revised form March 28, 1996.     

区间型符号数据的特征选择方法

对区间型符号数据进行特征选择,可以降低数据的维数,提取数据的关键特征。针对区间型符号数据的特征选择问题,本文提出了一种新的特征选择方法。首先,该方法使用区间数Hausdorff距离和区间数欧氏距离度量区间数的相似性,通过建立使得样本点与样本类中心相似性最大的优化模型来估计区间型符号数据的特征权重。其次,基于特征权重构建相应的分类器来评价所估计特征权重的优劣。最后,为了验证本文方法的有效性,分别在人工生成数据集和真实数据集上进行了数值实验,数值实验结果表明,本文方法可以有效地去除无关特征,识别出与类标号有关的特征。     

Optimal Mutual Location of Compact Sets in Spaces Endowed with Euclidean Invariant Gromov–Hausdorif Metric

Non–empty compact subsets of the Euclidean space located optimally (i.e., the Hausdorff distance between them cannot be decreased) are studied. It is shown that if one of them is a single point, then it is located at the Chebyshev center of the other one. Many other particular cases are considered too. As an application, it is proved that each three–point metric space cari be isometrically embedded into the orbit space of the group of proper motions acting on the compact subsets of the Euclidean space. In addition, it is proved that for each pair of optimally located compact subsets all intermediate compact sets in the sense of Hausdorff metric are also intermediate in the sense of Euclidean Gromov–Hausdorff metric.     

Normability via the Convergence of Closed and Convex Sets

The purpose of this short technical note is to show that a locally convex topological vector space is normable, if and only if an important convergence theorem about closed and convex sets holds. This new assumption of normability is related to the problem of preservation of Hausdorff lower continuity of the intersection of Hausdorff lower continuous, closed and convex valued correspondences.     

Efficient solutions in generalized linear vector optimization

This paper establishes several new facts on generalized polyhedral convex sets and shows how they can be used in vector optimization. Among other things, a scalarization formula for the efficient solution sets of generalized linear vector optimization problems is obtained. We also prove that the efficient solution set of a generalized linear vector optimization problem in a locally convex Hausdorff topological vector space is the union of finitely many generalized polyhedral convex sets and it is connected by line segments.     

Existence of solutions for generalized implicit vector variational-like inequalities

In this paper, we introduce a new class of generalized implicit vector variational-like inequalities in Hausdorff topological vector spaces and Banach spaces which contain implicit vector equilibrium problems, implicit vector variational inequalities and implicit vector complementarity problems as special cases. We derive some new results by using the KKM–Fan theorem, under compact and noncompact assumptions on underlying convex sets.     

On generalized variational inequalities

We obtain a new version of the minimax inequality of Ky Fan. As an application, an existence result for the generalized variational inequality problem with set-valued mappings defined on noncompact sets in Hausdorff topological vector spaces is given. Also, some existence results for the generalized variational inequality problem for quasimonotone and pseudomonotone mappings are obtained. Dedicated to the memory of T. Rapcsák.     

A family of kurtosis orderings for multivariate distributions

In this paper, a family of kurtosis orderings for multivariate distributions is proposed and studied. Each ordering characterizes in an affine invariant sense the movement of probability mass from the “shoulders” of a distribution to either the center or the tails or both. All even moments of the Mahalanobis distance of a random vector from its mean (if exists) preserve a subfamily of the orderings. For elliptically symmetric distributions, each ordering determines the distributions up to affine equivalence. As applications, the orderings are used to study elliptically symmetric distributions. Ordering results are established for three important families of elliptically symmetric distributions: Kotz type distributions, Pearson Type VII distributions, and Pearson Type II distributions.     

A characterization of the multivariate excess wealth ordering

In this paper, some new properties of the upper-corrected orthant of a random vector are proved. The univariate right-spread or excess wealth function, introduced by Fernández-Ponce et al. (1996), is extended to multivariate random vectors, and some properties of this multivariate function are studied. Later, this function was used to define the excess wealth ordering by Shaked and Shanthikumar (1998) and Fernández-Ponce et al. (1998). The multivariate excess wealth function enable us to define a new stochastic comparison which is weaker than the multivariate dispersion orderings. Also, some properties relating the multivariate excess wealth order with stochastic dependence are described.     

HAUSDORFF DIMENSION OF GENERALIZED STATISTICALLY SELF-AFFINE FRACTALS

The authors consider generalized statistically self-affine recursive fractals K with random numbers of subsets on each level. They obtain the Hausdorff dimensions of K without considering whether the subsets on each level are non-overlapping or not. They also give some examples to show that many important sets are the special cases of their models.     

Almost Sure Hausdorff Dimension of Graphs of Random Wavelet Series

In this contribution, we consider the problem of computing the Hausdorff dimension of the graph of a continuous random field obtained as an infinite series of smooth deterministic functions with independent random weights. The particular case of random wavelet series is addressed and almost sure lower bounds of their Hausdorff dimension are obtained. Sub-classes are exhibited for which these lower bounds coincide with almost sure upper bounds based on particular smoothness indices of the series. A direct application of these results provides new insights concerning the Hausdorff dimension as opposed to classical smoothness indices.     

The Hausdorff Dimension of Exceptional Sets Associated with Normal Forms

The Hausdorff dimension is obtained for exceptional sets associatedwith linearising a complex analytic diffeomorphism near a fixedpoint, and for related exceptional sets associated with obtaininga normal form of an analytic vector field near a singular point.The exceptional sets consist of eigenvalues which do not satisfya certain Diophantine condition and are ‘close’to resonance. They are related to ‘lim-sup’ setsof a general type arising in the theory of metric Diophantineapproximation and for which a lower bound for the Hausdorffdimension has been obtained.     

Hitting probabilities and the Hausdorff dimension of the inverse images of a class of anisotropic random fields

Let X = {X(t):t ∈ R~N} be an anisotropic random field with values in R~d.Under certain conditions on X,we establish upper and lower bounds on the hitting probabilities of X in terms of respectively Hausdorff measure and Bessel-Riesz capacity.We also obtain the Hausdorff dimension of its inverse image,and the Hausdorff and packing dimensions of its level sets.These results are applicable to non-linear solutions of stochastic heat equations driven by a white in time and spatially homogeneous Gaussian noise and anisotropic Guassian random fields.