Tempus volat,hora fugit: A survey of tie-oriented dynamic network models in discrete and continuous time

Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time. First, we introduce the temporal exponential random graph model (TERGM) and the separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the relational event model (REM). We additionally show how the REM can handle time-clustered observations, that is, continuous-time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models on two networks that represent international arms transfers and email exchange, respectively. The data allow to demonstrate the applicability and interpretation of the network models.     

Statistical inference for continuous-time Markov processes with block structure based on discrete-time network data

A widely used approach to modeling discrete-time network data assumes that discrete-time network data are generated by an unobserved continuous-time Markov process. While such models can capture a wide range of network phenomena and are popular in social network analysis, the models are based on the homogeneity assumption that all nodes share the same parameters. We remove the homogeneity assumption by allowing nodes to belong to unobserved subsets of nodes, called blocks, and assuming that nodes in the same block have the same parameters, whereas nodes in distinct blocks have distinct parameters. The resulting models capture unobserved heterogeneity across nodes and admit model-based clustering of nodes based on network properties chosen by researchers. We develop Bayesian data-augmentation methods and apply them to discrete-time observations of an ownership network of non-financial companies in Slovenia in its critical transition from a socialist economy to a market economy. We detect a small subset of shadow-financial companies that outpaces others in terms of the rate of change and the desire to accumulate stocks of other companies.     

最大度为10的图的列表全可染色问题

对于一个给定的最大度为10的平面图G,若图G不含相邻的三角形,则G是11-列表全可染色的,更进一步证明了列表全染色的猜想.     

Corporate investment, financing, and dividend policies in the high-tech industry

The agency theory, transactional cost economics, and traditional strategic management perspectives have provided several key corporate financial policies. However, these policies have generally been discussed and established separately. This study explores the causal structure of corporate financial strategies for the high-tech firms in Taiwan and China. By employing path analysis and directed graphs model, this paper explores the causal relationships among investment, financing, dividend policies, and corporate performance. The results show that the investment expenditures by Taiwan's firms positively affect financial performance and the increased borrowings jeopardize company's profits. However, the financing decisions of China's firms have a positively effect on their capital expenditures. The findings suggest that firms across the Strait adopt different strategies in financial decision environments.     

投资基金、资本市场及其因果分析

本文从宏观和市场发展层面上,利用格兰杰因果分析和有向非循环图（DAG）技术,对我国2002-2010年的证券投资基金与股票、M2、债券和居民存款之间的因果关系进行了分析。研究结果表明,我国金融市场仍处于分割状态,证券投资基金还没有有效地发挥联系渠道和资产优化配置的功能。证券投资基金存在着导致资本市场风险波动的羊群行为和惯性行为,这是对发展不完善的资本市场所作出的被动性的适应性反应。中国证券投资基金对股票市场具有显著性影响,规范和发展证券投资基金对促进股票市场健康发展具有重要作用。     

3—连通、3—正则图中的圈

本文介绍了3—连通、3—正则图中包含给定点集的大子集的圈。     

TH-A新型脂肪族高效减水剂研究

本文说明了TH-A新型脂肪族高效减水剂研制的生产工艺、原料配比,介绍了该产品的主要性能,总结了新工艺的主要特点.     

Macroeconomics of directed credit reforms in India

This paper develops a theoretical framework to evaluate directed credit reforms in India. It formulates a post-Keynesian/structuralist macro model that incorporates key features of the Indian banking system. The model divides the economy into a demand constrained industrial sector and a credit constrained agricultural sector. The model shows that directed credit reforms tighten agricultural credit and output, erode real wages by increasing the agricultural price and reduce industrial demand. Inflation also picks up on account of real wage resistance. This paper, therefore, has a close affinity with existing accounts that warn against the stagflationary consequences of financial liberalisation.     

The effect of short-form video on travel decision-making process: An integration of AIDA and MGB models

With the upsurge of individual interest in short-form video, this has become an important communication medium that has the potential to influence viewers' behavior. Based on the importance of understanding the relationship between short-form video and the travel decision-making process, this study attempted to examine this linkage in the context of Korean food by integrating the AIDA and MGB models. An online survey was conducted with the short-form video audience in China. A total of 505 responses were analyzed by using PLS-SEM. Based on the findings, this study verified the travel decision-making process in visiting Korea under the influence of Korean food short-form videos. This research contributes to the theoretical development of existing studies regarding short-form videos as well as the integration of the AIDA and MGB models. Moreover, practical insights are provided for destination marketers in development and utilization of Korean food short-form video as a marketing medium.     

Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

During the last years, graphical models have become a popular tool to represent dependencies among variables in many scientific areas. Typically, the objective is to discover dependence relationships that can be represented through a directed acyclic graph (DAG). The set of all conditional independencies encoded by a DAG determines its Markov property. In general, DAGs encoding the same conditional independencies are not distinguishable from observational data and can be collected into equivalence classes, each one represented by a chain graph called essential graph (EG). However, both the DAG and EG space grow super exponentially in the number of variables, and so, graph structural learning requires the adoption of Markov chain Monte Carlo (MCMC) techniques. In this paper, we review some recent results on Bayesian model selection of Gaussian DAG models under a unified framework. These results are based on closed-form expressions for the marginal likelihood of a DAG and EG structure, which is obtained from a few suitable assumptions on the prior for model parameters. We then introduce a general MCMC scheme that can be adopted both for model selection of DAGs and EGs together with a couple of applications on real data sets.