Line Hypergraphs: A Survey

The survey is devoted to line graphs and a new multivalued function called the line hypergraph. This function generalizes two classical concepts at once, namely the line graph and the dual hypergraph. In a certain sense, line graphs and dual hypergraphs are extreme values of the function . There are many publications about line graphs, but our considerations are restricted to papers concerning Krausz global characterization of line graphs or Whitneys theorem on edge isomorphisms. The survey covers almost all known results on the function because they are concentrated around Krausz and Whitneys theorems. These results provide evidence that the notion of the line hypergraph is quite natural. It enables one to unify the classical theorems on line graphs and to obtain their more general versions in a simpler way.     

基于时序图模型结构估计的股票联动研究

我国股市个股价格同时上涨或同时下跌的联动现象极为普遍,传统上使用向量自回归、协整、有向非循环图等方法主要用于少量股票或市场之间的联动性研究,不适于直接对大规模个股之间的联动关系进行研究。文章关注大规模时序图模型结构建立及估计方法,通过将ADL方法引入SPACE算法,提出了可以估计高维低样时序图模型的ADL-SPACE算法;设计模拟实验考察了算法中惩罚参数λ值的设置对于节点自回归相关性捕获的有效性;在实证研究中,文章使用了ADL-SPACE算法对个股联动研究了三方面的内容:1.基于个股联动的代表性行业之间的联动性;2.设计了我国A股市场中行业联动强度,对行业内外联动性进行综合评价和分析;3.采用一阶滞后个股基于时序图模型结果构造了投资组合,模拟显示收益预期表现良好。以上研究均表明时序SPACE图模型方法在大规模股票的联动探测中有较好的应用前景。     

A Test for Nonlinearity of Time Series with Infinite Variance

A heavy tailed time series that can be represented as an infinite moving average has the property that the sample autocorrelation function (ACF) at lag h converges in probability to a constant (h), although the mathematical correlation typically does not exist. For many nonlinear heavy tailed models, however, the sample ACF at lag h converges in distribution to a nondegenerate random variable. In this paper, a test for (non)linearity of a given infinite variance time series is constructed, based on subsample stability of the sample ACF. The test is applied to several real and simulated datasets.     

Dissimilarity Plots: A Visual Exploration Tool for Partitional Clustering

For hierarchical clustering, dendrograms are a convenient and powerful visualization technique. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this article we extend (dissimilarity) matrix shading with several reordering steps based on seriation techniques. Both ideas, matrix shading and reordering, have been well known for a long time. However, only recent algorithmic improvements allow us to solve or approximately solve the seriation problem efficiently for larger problems. Furthermore, seriation techniques are used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is able to present the structure between clusters and the micro-structure within clusters in one concise plot. This not only allows us to judge cluster quality but also makes misspecification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples. Experiments show that dissimilarity plots scale very well with increasing data dimensionality.

Supplemental materials with additional experiments for this article are available online.     

探测方向性时间序列的相依性的标准偏自相关

我们建立了一个方法去探测线性与方向性时间序列的分段相依。Ｊｕｐｐ＆Ｍａｒｄｉｎ提出了流形上二元分布和二元角分布的相关函数。基于他们的工作，我们对方向性时间序列的自相关函数及偏相关函数提出了新的定义，而且我们用这些新的定义做了一些模拟工作，并应用在实际的数据上，得到了一些令人满意的结果。     

Municipal Water Demand Forecasting: Tools for Intervention Time Series

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.     

A Score Type Test for General Autoregressive Models in Time Series

This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.     

A Nonparametric Test of Serial Independence for Time Series and Residuals

This paper presents nonparametric tests of independence that can be used to test the independence of p random variables, serial independence for time series, or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression residuals are discussed.     

Generalized Rank Estimates For An Autoregressive Time Series: A U-Statistic Approach

A class of weighted rank-based estimates for estimating the parameter vector of an autoregressive time series is considered. This class of estimates is similar to, and contains, the class proposed by Terpstra et al. [54]. Asymptotic linearity properties are derived for the so called GR-estimates. Based on these properties, the GR-estimates are shown to be asymptotically normal at rate n ^1/2. The theory of U-statistics along with a characterization of weak dependence that is inherent in stationary AR(p) models are the primary tools used to obtain the results. The so called pair-wise slopes estimator, which is a special case of this class of estimates, is discussed in an AR(1) context. This revised version was published online in June 2006 with corrections to the Cover Date.     

A Linear Time Algorithm for the Minimum-weight Feedback Vertex Set Problem in Series-parallel Graphs

A feedback vertex set is a subset of vertices in a graph, whose deletion from the graph makes the resulting graph acyclic. In this paper, we study the minimum-weight feedback vertex set problem in seriesparallel graphs and present a linear-time exact algorithm to solve it.     

A Closure for 1‐Hamilton‐Connectedness in Claw‐Free Graphs

A graph G is 1‐Hamilton‐connected if is Hamilton‐connected for every vertex . In the article, we introduce a closure concept for 1‐Hamilton‐connectedness in claw‐free graphs. If is a (new) closure of a claw‐free graph G, then is 1‐Hamilton‐connected if and only if G is 1‐Hamilton‐connected, is the line graph of a multigraph, and for some , is the line graph of a multigraph with at most two triangles or at most one double edge. As applications, we prove that Thomassen's Conjecture (every 4‐connected line graph is hamiltonian) is equivalent to the statement that every 4‐connected claw‐free graph is 1‐Hamilton‐connected, and we present results showing that every 5‐connected claw‐free graph with minimum degree at least 6 is 1‐Hamilton‐connected and that every 4‐connected claw‐free and hourglass‐free graph is 1‐Hamilton‐connected.     

Time and inventory dependent optimal maintenance policies for single machine workstations: An MDP approach

In this work the problem of obtaining an optimal maintenance policy for a single-machine, single-product workstation that deteriorates over time is addressed, using Markov Decision Process (MDP) models. Two models are proposed. The decision criteria for the first model is based on the cost of performing maintenance, the cost of repairing a failed machine and the cost of holding inventory while the machine is not available for production. For the second model the cost of holding inventory is replaced by the cost of not satisfying the demand. The processing time of jobs, inter-arrival times of jobs or units of demand, and the failure times are assumed to be random. The results show that in order to make better maintenance decisions the interaction between the inventory (whether in process or final), and the number of shifts that the machine has been working without restoration, has to be taken into account. If this interaction is considered, the long-run operational costs are reduced significantly. Moreover, structural properties of the optimal policies of the models are obtained after imposing conditions on the parameters of the models and on the distribution of the lifetime of a recently restored machine.     

时间序列模型L_1-估计量的极限分布:平稳自回旧模型

时间序列模型的L_1-估计问题是非常重要的．这些估计量的许多性质都被研究过．然而,仍有一些基本问题有待解决．本文将给出平稳自回归模型L_1-估计量的极限分布．     

The signal model: A model for competing risks of opportunistic maintenance

This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.     

A Class of Sparse Invertible Matrices and Their Use for Nonlinear Prediction of Nearly Periodic Time Series with Fixed Period

We introduce a class of sparse matrices U _m (A _{p 1} ) of order m by m, where m is a composite natural number, p ₁ is a divisor of m, and A _{p 1} is a set of nonzero real numbers of length p ₁. The construction of U _m (A _{p 1} ) is achieved by iteration, involving repetitive dilation operations and block-matrix operations. We prove that the matrices U _m (A _{p 1} ) are invertible and we compute the inverse matrix (U _m (A _{p 1} ))^?1 explicitly. We prove that each row of the inverse matrix (U _m (A _{p 1} ))^?1 has only two nonzero entries with alternative signs, located at specific positions, related to the divisors of m. We use the structural properties of the matrix (U _m (A _{p 1} ))^?1 in order to build a nonlinear estimator for prediction of nearly periodic time series of length m with fixed period.     

A defect-correction method for unsteady conduction-convection problems II: Time discretization

In this paper, a fully discrete defect-correction mixed finite element method (MFEM) for solving the non-stationary conduction-convection problems in two dimension, which is leaded by combining the Back Euler time discretization with the two-step defect correction in space, is presented. In this method, we solve the nonlinear equations with an added artificial viscosity term on a finite element grid and correct these solutions on the same grid using a linearized defect-correction technique. The stability and the error analysis are derived. The theory analysis shows that our method is stable and has a good convergence property. Some numerical results are also given, which show that this method is highly efficient for the unsteady conduction-convection problems.     

Probability chains: A general linearization technique for modeling reliability in facility location and related problems

In this paper, we propose an efficient technique for linearizing facility location problems with site-dependent failure probabilities, focusing on the unreliable p-median problem. Our approach is based on the use of a specialized flow network, which we refer to as a probability chain, to evaluate compound probability terms. The resulting linear model is compact in size. The method can be employed in a straightforward way to linearize similarly structured problems, such as the maximum expected covering problem. We further discuss how probability chains can be extended to problems with co-location and other, more general problem classes. Additional lower bounds as well as valid inequalities for use within a branch and cut algorithm are introduced to significantly speed up overall solution time. Computational results are presented for several test problems showing the efficiency of our linear model in comparison to existing problem formulations.     

A case study of cracks in the scientific enterprise: Reinvention of information‐theoretic measures for graphs

This article calls attention to flaws in the scientific enterprise, providing a case study of the lack of professionalism in published journal articles in a particular area of research, namely, network complexity. By offering details of a special case of poor scholarship, which is very likely indicative of a broader problem, the authors hope to stimulate editors and referees to greater vigilance, and to strengthen authors' resolve to take their professional responsibilities more seriously. © 2014 Wiley Periodicals, Inc. Complexity 21: 10–14, 2016