A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs

Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this article, we consider a semiparametric problem of two-sample hypothesis testing for a class of latent position random graphs. We formulate a notion of consistency in this context and propose a valid test for the hypothesis that two finite-dimensional random dot product graphs on a common vertex set have the same generating latent positions or have generating latent positions that are scaled or diagonal transformations of one another. Our test statistic is a function of a spectral decomposition of the adjacency matrix for each graph and our test procedure is consistent across a broad range of alternatives. We apply our test procedure to real biological data: in a test-retest dataset of neural connectome graphs, we are able to distinguish between scans from different subjects; and in the C. elegans connectome, we are able to distinguish between chemical and electrical networks. The latter example is a concrete demonstration that our test can have power even for small-sample sizes. We conclude by discussing the relationship between our test procedure and generalized likelihood ratio tests. Supplementary materials for this article are available online.     

A higher order Markov model for analyzing covariate dependence

During the recent past, there has been a renewed interest in Markov chain for its attractive properties for analyzing real life data emerging from time series or longitudinal data in various fields. The models were proposed for fitting first or higher order Markov chains. However, there is a serious lack of realistic methods for linking covariate dependence with transition probabilities in order to analyze the factors associated with such transitions especially for higher order Markov chains. L.R. Muenz and L.V. Rubinstein [Markov models for covariate dependence of binary sequences, Biometrics 41 (1985) 91–101] employed logistic regression models to analyze the transition probabilities for a first order Markov model. The methodology is still far from generalization in terms of formulating a model for higher order Markov chains. In this study, it is aimed to provide a comprehensive covariate-dependent Markov model for higher order. The proposed model generalizes the estimation procedure for Markov models for any order. The proposed models and inference procedures are simple and the covariate dependence of the transition probabilities of any order can be examined without making the underlying model complex. An example from rainfall data is illustrated in this paper that shows the utility of the proposed model for analyzing complex real life problems. The application of the proposed method indicates that the higher order covariate dependent Markov models can be conveniently employed in a very useful manner and the results can provide in-depth insights to both the researchers and policymakers to resolve complex problems of underlying factors attributing to different types of transitions, reverse transitions and repeated transitions. The estimation and test procedures can be employed for any order of Markov model without making the theory and interpretation difficult for the common users.     

Simultaneous confidence band and hypothesis test in generalised varying-coefficient models

Generalised varying-coefficient models (GVC) are very important models. There are a considerable number of literature addressing these models. However, most of the existing literature are devoted to the estimation procedure. In this paper, we systematically investigate the statistical inference for GVC, which includes confidence band as well as hypothesis test. We establish the asymptotic distribution of the maximum discrepancy between the estimated functional coefficient and the true functional coefficient. We compare different approaches for the construction of confidence band and hypothesis test. Finally, the proposed statistical inference methods are used to analyse the data from China about contraceptive use there, which leads to some interesting findings.     

Asymptotic Expansions of Transition Densities for Hybrid Jump-diffusions

A class of hybrid jump diffusions modulated by a Markov chain is considered in this work.Themotivation stems from insurance risk models,and emerging applications in production planning and wirelesscommunications.The models are hybrid in that they involve both continuous dynamics and discrete events.Under suitable conditions,asymptotic expansions of the transition densities for the underlying processes aredeveloped.The formal expansions are validated and the error bounds obtained.     

Partially Observable Markov Decision Processes incorporating epistemic uncertainties

The use of Markov Decision Processes for Inspection Maintenance and Rehabilitation of civil engineering structures relies on the use of several transition matrices related to the stochastic degradation process, maintenance actions and imperfect inspections. Point estimators for these matrices are usually used and they are evaluated using statistical inference methods and/or expert evaluation methods. Thus, considerable epistemic uncertainty often veils the true values of these matrices. Our contribution through this paper is threefold. First, we present a methodology for incorporating epistemic uncertainties in dynamic programming algorithms used to solve finite horizon Markov Decision Processes (which may be partially observable). Second, we propose a methodology based on the use of Dirichlet distributions which answers, in our sense, much of the controversy found in the literature about estimating Markov transition matrices. Third, we show how the complexity resulting from the use of Monte-Carlo simulations for the transition matrices can be greatly overcome in the framework of dynamic programming. The proposed model is applied to concrete bridge under degradation, in order to provide the optimal strategy for inspection and maintenance. The influence of epistemic uncertainties on the optimal solution is underlined through sensitivity analysis regarding the input data.     

The minimal solution of a Markov renewal equation

Given a killed Markov process, one can use a procedure of Ikedaet al. to revive the process at the killing times. The revived process is again a Markov process and its transition function is the minimal solution of a Markov renewal equation. In this paper we will calculate such solutions for a class of revived processes.     

Studying an overload system using rotation

1. IntroductionThe motivation of writing this paper was from calculating the blocking probability foran overloaded finite system. Our numerical experiments suggested that this probability canbe approximated efficiently by rotating the transition matrix by 180". Some preliminaryresults were obtained and can be found in [11 and [2]. Rotating the transition matrix definesa new Markov chain, which is often called the dual process in the literature, for example,[3--7]. For a finite Markov chain, …     

Kolmogorov equations in fractional derivatives for the transition probabilities of continuous-time Markov processes

A family of one-dimensional continuous-time Markov processes is considered, for which the author has earlier determined the transition probabilities by directly solving the Kolmogorov–Chapman equation; these probabilities have the form of single integrals. Analogues of the first and second Kolmogorov equations for the family of processes under consideration are obtained by using a procedure for obtaining integro-differential equations describing Markov processes with discontinuous trajectories. These equations turn out to be equations in fractional derivatives. The results are based on an asymptotic analysis of the transition probability as the start and end times of the transition approach each other. This analysis implies that the trajectories of a given Markov process are divided into two classes, depending on the interval in which they start. Some of the trajectories decay during a short time interval with a certain probability, and others are generated with a certain probability.     

A Bivariate Model for Markov Manpower Planning Systems

Markov models are being extensively used for analysis of manpower planning systems. Most of these models concentrate either on estimating the gradewise distribution of future manpower structure, given the existing structure and promotion policies, or on deriving policies towards promotion, given the required future structure. However, in many large organizations, agreements between employee unions and management result in the framing of policies towards promotion based either on seniority (length of service in the grade) or on performance (as in the case of ‘high fliers’). In this paper these two criteria are considered in a bivariate distribution framework. The transition probabilities for promotion obtained from the Markov model are further translated into required seniority and performance rating. The procedure is illustrated through an example.     

Organization and efficiency in the international insurance industry: A cross-frontier analysis

This paper employs cross-frontier analysis, an innovative tool based on data envelopment analysis, to provide new insight into the relationship between organization and efficiency in international insurance markets. We are the first to empirically test the expense preference hypothesis and the efficient structure hypothesis in a large cross-country study. For this purpose, we consider 23,807 firm-years for 21 countries from northern America and the European Union—a dataset not previously analyzed in this context. We find evidence for the efficient structure hypothesis in selected market segments, but we find no evidence for the expense preference hypothesis. Our results provide insight into the competitiveness of stock and mutual insurers from different countries. At the country level, the results can be used to compare different insurance markets. Our findings are especially interesting for the strategic management of insurance companies as well as for regulators and boards of national insurance associations.     

Censoring,Factorizations, and Spectral Analysis for Transition Matrices with Block-Repeating Entries

In this paper, we use the Markov chain censoring technique to study infinite state Markov chains whose transition matrices possess block-repeating entries. We demonstrate that a number of important probabilistic measures are invariant under censoring. Informally speaking, these measures involve first passage times or expected numbers of visits to certain levels where other levels are taboo; they are closely related to the so-called fundamental matrix of the Markov chain which is also studied here. Factorization theorems for the characteristic equation of the blocks of the transition matrix are obtained. Necessary and sufficient conditions are derived for such a Markov chain to be positive recurrent, null recurrent, or transient based either on spectral analysis, or on a property of the fundamental matrix. Explicit expressions are obtained for key probabilistic measures, including the stationary probability vector and the fundamental matrix, which could be potentially used to develop various recursive algorithms for computing these measures.     

A Markov Renewal Approach to M/G/1 Type Queues with Countably Many Background States

We consider the stationary distribution of the M/GI/1 type queue when background states are countable. We are interested in its tail behavior. To this end, we derive a Markov renewal equation for characterizing the stationary distribution using a Markov additive process that describes the number of customers in system when the system is not empty. Variants of this Markov renewal equation are also derived. It is shown that the transition kernels of these renewal equations can be expressed by the ladder height and the associated background state of a dual Markov additive process. Usually, matrix analysis is extensively used for studying the M/G/1 type queue. However, this may not be convenient when the background states are countable. We here rely on stochastic arguments, which not only make computations possible but also reveal new features. Those results are applied to study the tail decay rates of the stationary distributions. This includes refinements of the existence results with extensions.     

Analysis of variance with general errors and grouped and non-grouped data: Some iterative algorithms

In this paper we consider some iterative estimation algorithms, which are valid to analyse the variance of data, which may be either non-grouped or grouped with different classification intervals. This situation appears, for instance, when data is collected from different sources and the grouping intervals differ from one source to another. The analysis of variance is carried out by means of general linear models, whose error terms may be general. An initial procedure in the line of the EM, although it does not necessarily agree with it, opens the paper and gives rise to a simplified version where we avoid the double iteration, which implicitly appears in the EM and, also, in the initial procedure mentioned above. The asymptotic stochastic properties of the resulting estimates have been investigated in depth and used to test ANOVA hypothesis.     

信号检测问题的假设检验:大偏差方法

本文研究了马氏链的假设检验问题.利用大偏差的方法,获得了马氏链的假设检验的否定域,并得到了犯第二二类错误概率的衰减性.     

尖劈吸波体与导引仿真用微波暗室的性能研究

首先,针对尖劈形状吸波体的性能问题,给出了直接计算法和基于镜像模型的方法,并对其进行了对比计算与仿真.其次,对于微波暗室的性能研究,针对不同的复杂度要求,建立了两种数学模型—射线追踪(Ray Tracing)模型和基于Markov链的有限元(FEM,Finite Element Model)模型.建模过程和仿真结果表明,Ray Tracing模型的计算复杂度较低,但电磁波"镜面反射"的假设过于理想,模型较为粗糙,只能用于粗略模拟实际情况.而基于Markov链的FEM模型较Ray Tracing模型更加精确.同时,相比于传统的具有高计算复杂度的FEM模型,基于Markov链的FEM模型计算更加简便,利于计算机仿真实现,而且不降低FEM模型的精确度,可以精确模拟实际情况.     

Decision diagrams for the approximate analysis of Markov models

Decision diagrams of various types can be used to encode the exact state space and transition rate matrix of large Markov models. However, the exact solution of such models still requires to store at least one real vector with one entry per reachable state, a formidable limitation to the practical use of these encodings. Thus, we discuss automatic techniques for the approximate computation of performance measures when the Markov model can be compactly encoded but not exactly solved. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

客户回报随机过程的Markov性和时间齐次性

在客户发展关系的Markov链模型的基础上,构建了企业的客户回报随机过程．证明了：在适当假设下,客户回报过程是Markov链。甚至是时间齐次的Markov链．本文求出了该链的转移概率．通过转移概率得到了客户给企业期望回报的一些计算公式,从而为企业选定发展客户关系策略提供了有效的量化基础．     

Hitting Times in Markov Chains with Restart and their Application to Network Centrality

Motivated by applications in telecommunications, computer science and physics, we consider a discrete-time Markov process with restart. At each step the process either with a positive probability restarts from a given distribution, or with the complementary probability continues according to a Markov transition kernel. The main contribution of the present work is that we obtain an explicit expression for the expectation of the hitting time (to a given target set) of the process with restart. The formula is convenient when considering the problem of optimization of the expected hitting time with respect to the restart probability. We illustrate our results with two examples in uncountable and countable state spaces and with an application to network centrality.     

Marginal AMP chain graphs

We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them is Markov equivalent to some AMP chain graph under marginalization of some of its nodes. However, MAMP chain graphs do not only subsume AMP chain graphs but also multivariate regression chain graphs. We describe global and pairwise Markov properties for MAMP chain graphs and prove their equivalence for compositional graphoids. We also characterize when two MAMP chain graphs are Markov equivalent.For Gaussian probability distributions, we also show that every MAMP chain graph is Markov equivalent to some directed and acyclic graph with deterministic nodes under marginalization and conditioning on some of its nodes. This is important because it implies that the independence model represented by a MAMP chain graph can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we modify MAMP chain graphs so that they are closed under marginalization for Gaussian probability distributions. This is a desirable feature because it guarantees parsimonious models under marginalization.