次序一致性判断矩阵的相关理论及排序

给出了完全次序一致性的定义和次序一致性矩阵的标准形式,并证明了满意一致性与次序一致性的等价性,然后给出了同时适用于互反与互补两种判断矩阵的完全次序一致性检验及改进的交互式算法,最后在次序一致性的基础上给出了模糊互补判断矩阵排序的一种新方法,并给出了一个算例.     

Logistic映射三周期轨道分支值确定

运用分支函数的方法研究了Logistic映射出现稳定三周期点的参数窗口,借助软件Singular 2.0.3运用多元多项式的Grobner基并结合严格局部分析的方法给出Logistic映射出现稳定三周期点时的参数值范围,最后数值模拟方法对所得的结果进行了验证,并指出伪轨现象.     

我国农村居民消费现状及影响因素分析

我国经济虽然实现了高速增长,但城乡居民消费差距不仅没有缩小,反而在继续扩大,由占人口绝对多数的农村居民的消费水平过低所直接导致的我国消费需求不足已成为目前我国宏观经济中最突出的矛盾。本文在农村居民消费结构的基础上,分析影响居民消费的其他原因,以及这些因素与消费的相关程度,建立回归模型,并采用2007年中国农村居民的收入消费等统计资料,对消费状况进行实证分析。最后本文针对我国农村居民现实的消费状况提出针对性的建议。     

不确定偏好序下的双边匹配博弈

针对带有不确定偏好序的双边匹配问题,现有方法大都仅注重整体收益之和,忽略了参与人的个体收益以及在交互选择中的策略运用.基于最大满意度准则,给出不确定序下的收益（满意度）矩阵的推导过程;然后,从个体理性视角,结合矩阵博弈的思想构建一种兼顾整体和个体收益的博弈匹配优化模型,并证明模型最优解满足纳什均衡.最后,进一步探讨各种策略选择及其优劣分析.     

Analysis of ordered categorical data to develop control charts for monitoring customer loyalty

In the highly competitive business environment of today, the cost to attract new customers is much higher than the cost required to maintain the existing ones. To keep the balance between the acquisition rate and defection rate through executing offensive and defensive marketing policies, it is required to have real time information using an efficient method to monitor customer loyalty. The relationship between customer loyalty and customer satisfaction should be kept in mind when one develops a method for loyalty monitoring. This paper presents several control charts classified in two groups based on the scale used to assess customer loyalty. In the first group of control charts, customer loyalty is considered as a binary random variable modeled by Bernoulli distribution whilst in the second group, an ordinal scale is considered to report loyalty level. Performance comparison of the proposed techniques using ARL criterion indicates that chi‐square and likelihood‐ratio control charts developed based on Pearson chi‐square statistic and ordinal logistic regression model respectively are able to rapidly detect the significant changes in loyalty behavior. To show how to apply the procedures and how to interpret their results, two illustrative synthetic cases are also explained. Copyright © 2009 John Wiley & Sons, Ltd.     

Learning Interactions via Hierarchical Group-Lasso Regularization

We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be nonzero, both its associated main effects are also included in the model. We motivate our approach by modeling pairwise interactions for categorical variables with arbitrary numbers of levels, and then show how we can accommodate continuous variables as well. Our approach allows us to dispense with explicitly applying constraints on the main effects and interactions for identifiability, which results in interpretable interaction models. We compare our method with existing approaches on both simulated and real data, including a genome-wide association study, all using our R package glinternet.     

Shape-Constrained Estimation Using Nonnegative Splines

We consider the problem of nonparametric estimation of unknown smooth functions in the presence of restrictions on the shape of the estimator and on its support using polynomial splines. We provide a general computational framework that treats these estimation problems in a unified manner, without the limitations of the existing methods. Applications of our approach include computing optimal spline estimators for regression, density estimation, and arrival rate estimation problems in the presence of various shape constraints. Our approach can also handle multiple simultaneous shape constraints. The approach is based on a characterization of nonnegative polynomials that leads to semidefinite programming (SDP) and second-order cone programming (SOCP) formulations of the problems. These formulations extend and generalize a number of previous approaches in the literature, including those with piecewise linear and B-spline estimators. We also consider a simpler approach in which nonnegative splines are approximated by splines whose pieces are polynomials with nonnegative coefficients in a nonnegative basis. A condition is presented to test whether a given nonnegative basis gives rise to a spline cone that is dense in the space of nonnegative continuous functions. The optimization models formulated in the article are solvable with minimal running time using off-the-shelf software. We provide numerical illustrations for density estimation and regression problems. These examples show that the proposed approach requires minimal computational time, and that the estimators obtained using our approach often match and frequently outperform kernel methods and spline smoothing without shape constraints. Supplementary materials for this article are provided online.     

Fast Nonparametric Quantile Regression With Arbitrary Smoothing Methods

The calculation of nonparametric quantile regression curve estimates is often computationally intensive, as typically an expensive nonlinear optimization problem is involved. This article proposes a fast and easy-to-implement method for computing such estimates. The main idea is to approximate the costly nonlinear optimization by a sequence of well-studied penalized least squares-type nonparametric mean regression estimation problems. The new method can be paired with different nonparametric smoothing methods and can also be applied to higher dimensional settings. Therefore, it provides a unified framework for computing different types of nonparametric quantile regression estimates, and it also greatly broadens the scope of the applicability of quantile regression methodology. This wide applicability and the practical performance of the proposed method are illustrated with smoothing spline and wavelet curve estimators, for both uni- and bivariate settings. Results from numerical experiments suggest that estimates obtained from the proposed method are superior to many competitors. This article has supplementary material online.     

On multidimensional discrete-time Beverton–Holt competition models

Following Ackleh et al. (2005), we study the multidimensional discrete-time competitive Beverton–Holt equations with equal interspecific competition coefficients. It is shown that competitive exclusion occurs if only one species has the largest carrying capacity. Otherwise, all the species with the largest carrying capacity coexist. In the former case, the system is globally asymptotically stable. In the latter case, the system has a linear stable manifold.