基于Vague相似度量理论的建筑设计方案评价方法

描述了Vague之间的相似度量与加权相似度量方法,以及建筑设计方案决策新方法.在建筑设计方案决策中,Vague之间的相似度量是评价建筑设计方案接近期望方案的测度,相似度量值越大,建筑设计方案接近期望方案越好.     

Partitioned difference families and almost difference sets

Partitioned difference families (PDFs) were first studied by Ding and Yin in conjunction with the construction of constant composition codes (CCCs). In 2008, Yin et al. presented the constructions of a number of infinite classes of PDFs based on known difference sets in GF(q). In this paper, we further investigate the constructions of PDFs by using known almost difference sets in GF(q), and establish some recursive constructions of PDFs. As their applications, we also get a number of perfect difference systems of sets (DSSs) over Z_q² with q odd prime.     

非良基集与共代数理论研究

作为经典集合论的扩张,非良基集合论具有更为丰富的集合全域。它能够提供一套完备的工具刻画现实世界中众多的循环现象。共代数是代数的对偶概念。在非良基的范畴下,使用共代数方法,可以方便地构造各种循环现象在集合理论上的模型。因此,对非良基集及共代数理论的研究极具吸引力。     

The existence of almost difference families

Almost difference families (ADFs) were introduced by Ding and Yin as a useful generalization of almost difference sets (ADSs), and a number of infinite classes of almost difference families had been constructed. Suppose q is a prime power. To construct combinatorial designs in GF(q), one often needs to find an element xGF(q){0}, such that some polynomials in GF(q)[x] of degree one or two satisfying certain conditions. Weil's theorem on character sum estimates is very useful to do this. In this paper, a general bound for finding such x is given. By using this bound and computer searching, some known results on almost difference families by Ding and Yin are improved.     

Kolmogorov-smirnov confidence sets: the case of more than one sample

For the two-sample problem, distribution-free confidence sets for the shift parameter when the scale parameters are equal and for both the shift and the ratio of scale parameters are derived. Multiple comparisons for the k sample location problem are constructed when all scale parameters are equal. Examples are given. Procedures may be completed with only pencil and paper.

     

Application of copulas to multivariate control charts

The most popular multivariate control chart for monitoring the mean of a distribution is probably the Hotelling T² rule. Unfortunately, this rule relies on the assumption that the distribution under control is Gaussian, which is rarely true in practice. The objective of this paper is to propose a new approach for the non-normal multivariate case. It consists in the construction of a tolerance region obtained from a density level set estimation. The method follows a “plug-in” approach in which the density of the observations is previously estimated. This estimation is conducted using copulas modeling, an increasingly popular tool in multivariate modeling.     

Variable Selection for Kernel Classification

In this article, a variable selection procedure, called surrogate selection, is proposed which can be applied when a support vector machine or kernel Fisher discriminant analysis is used in a binary classification problem. Surrogate selection applies the lasso after substituting the kernel discriminant scores for the binary group labels, as well as values for the input variable observations. Empirical results are reported, showing that surrogate selection performs well.     

Stochastic ordering and a class of multivariate new better than used distributions

A new characterization for the univariate class of new better than used ‘NBU’ distributions in terms of stochastic ordering is introduced. A multivariate version of this characterization is then used to define a multivariate class of NBU distributions. Basic properties of this class are derived. Comparisons and relationships of this new class with earlier classes are developed. Two multivariate new worse than used (NWU) classes of life distributions are defined and compared and their basic properties are studied.     

International Statistical Activities

The growth of the health care industry places increasing strain on available resources. As in other areas of social policy, health statisticians and health data are increasingly expected to provide keys to rational decision making. To accomplish this goal, the statistician and decision-maker need to interact to an increasing degree. The current issues in health policy and the statistician's contribution to policy analysis are discussed in the context of the National Center for Health Statistics experience. While the article focuses on health, it has implications for statisticians and policy formation in other fields.