Regression analysis and dependence

In this paper we study the relationship between regression analysis and a multivariate dependency measure. If the general regression model Y=f() holds for some function f, where 1i₁< i₂<···i_m k, and X₁,...,X_k is a set of possible explanatory random variables for Y. Then there exists a dependency relation between the random variable Y and the random vector (). Using the dependency statistic defined below, we can detect such dependency even if the function f is not linear. We present several examples with real and simulated data to illustrate this assertion. We also present a way to select the appropriate subset among the random variables X₁,X₂,...,X_k, which better explain Y.     

Statistical analysis of change in networks

A survey is given of random graphs and random graph processes which can be used to describe and analyze networks that are changing with time. Marko-vian change over time, log-linear models for change, and conditionally uniform models for change are described. It is noted that estimation is usually complex if the random graph involves dependent dyads. Models with deterministic change over time may be a way to avoid the difficulties implied by dependent dyads. Logit regression methods are described that can be used to estimate such models.     

Interactive analysis of high-dimensional association structures with graphical models

Graphical chain models are a powerful tool for analyzing multivariate data. Their practical use may still be cumbersome in some respects, since fitting the model requires a lengthy selection strategy based on the calculation of an enormous number of different regressions. In this paper, we present a computer system especially designed for the calculation of graphical chain models, which will not only automatically carry out the model search but also visualize the corresponding graph at each stage of the model fit. In addition, it allows the user to modify the graph and to fit the model interactively.     

Social Structural Inquiries – Part 2: Statistical Methods and Process Models

A tightened linkage between theory and data would enhance cumulative sociological knowledge. Toward that end this article selectivity reviews and develops social structural theories – theories that explain data. It focuses on statistical methods and process models because both approaches advance cumulative social science, and the tension between their advocates works against disciplinary solidarity. Boudon’s structural schematics cover all of the examples, suggesting a common perspective that can lessen the friction among practitioners of these forms of quantitative analysis and refocus much of the diversity of current sociology. An erratum to this article is available at .     

输油管布置的数学模型

做好布设的安全性和经济费用的花销是输油管布设的关键,本文就安全经济布置对输油管的线路进行了分析和研究。     

The Problem of Time Dependent Explanatory Variables at the Context-Level in Discrete Time Multilevel Event History Analysis: A Comparison of Models Considering Mobility Between Local Labour Markets as an Example

The paper deals with the question of how to include time dependent explanatory variables at the context-level in multilevel event history models. In general, context-level explanatory variables in multilevel models are assumed to be time constant. Only time constant context-level explanatory variables perform the task of reducing context-level error variance. Thus, it will be suggested that the analysis should be extended to a three-level model. In this model, time periods of persons constitute level 1 units, time periods of contexts constitute level 2 units and the contexts themselves constitute level 3 units – in which in turn level 2 units are clustered. Considering mobility between local labour markets as an example, four different ways of modelling time varying context-level variables are compared. The result is that the proposed three-level model leads to the most conservative results.     

Information visualisation based on graph models

Information visualisation is a key component of support tools for many applications in science and engineering. A graph is an abstract structure that is widely used to model information for its visualisation. In this paper, we consider practical and general graph formalism called hierarchical graphs and present the Higres and Visual Graph systems aimed at supporting information visualisation on the base of hierarchical graph models.     

基于SNA的IdeaStorm众包创新社区网络结构特征研究

作为企业开放式创新的重要实现平台,众包创新虚拟社区的参与用户数量众多、来源广泛,用户之间的互动行为与关系强度对众包创新绩效起着重要作用。以典型众包创新虚拟社区——戴尔公司IdeaStorm为例,运用社会网络分析方法(Social Network Analysis,SNA)研究该社区的网络结构特征,探测参与用户的互动行为模式。结果显示,IdeaStorm众包创新社区中的大部分用户为边缘用户,只有少数用户积极参与互动;小世界效应反映出再网络结构中用户之间的知识传播较为快捷,但互动强度不够。上述研究对理解众包创新虚拟社区的用户异质性行为,引导用户积极参与,进而提升众包创新绩效具有指导意义。     

p2: a random effects model with covariates for directed graphs

A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph, using nodal and/or dyadic attributes as covariates. The network structure is reflected by modeling the dependence between the relations to and from the same actor or node. Parameter estimates are proposed that are based on an iterated generalized least-squares procedure. An application is presented to a data set on friendship relations between American lawyers.     

RANDOM WALKS ON REGULAR POLYHEDRA AND OTHER DISTANCE–REGULAR GRAPHS

In this paper we consider Markov chains of the following type: the state space is the set of vertices of a connected, regular graph, and for each vertex transitions are to the adjacent vertices, with equal probabilities. When the mean first–passage matrix F of such a Markov chain is symmetric, the expectation and variance of first–entrance times, recurrence times, number of visits to a vertex and the expectation of the number of different vertices visited, can easily be computed from the entries of F. The method is most effective, when the underlying graph is distance–regular; then F is symmetric and the entries of F can easily be obtained from the graph.