Model-based clustering for social networks

Summary.  Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, the latent position cluster model , under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean 'social space', and the actors' locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster. We propose two estimation methods: a two-stage maximum likelihood method and a fully Bayesian method that uses Markov chain Monte Carlo sampling. The former is quicker and simpler, but the latter performs better. We also propose a Bayesian way of determining the number of clusters that are present by using approximate conditional Bayes factors. Our model represents transitivity, homophily by attributes and clustering simultaneously and does not require the number of clusters to be known. The model makes it easy to simulate realistic networks with clustering, which are potentially useful as inputs to models of more complex systems of which the network is part, such as epidemic models of infectious disease. We apply the model to two networks of social relations. A free software package in the R statistical language, latentnet, is available to analyse data by using the model.     

Using Related Samples in Assessing Conformance to Safety Goals: A Nuclear Reactor Safety Application

Quantifying safety goals is a key to the regulation of activities which are beneficial on the whole but entail some risks in being performed. Determining compliance with safety goals involves dealing with uncertainties. A recent article by Bier^(I) describes some of the difficulties encountered using measures with uncertainty to determine compliance with safety goals for nuclear reactors. This paper uses a hierarchical Bayes approach to address two practical modeling problems in determining safety goal compliance under uncertainty: (1) allowing some modeling assumptions to be relaxed, and (2) allowing data from previous related samples to be included in the analysis. The two issues effect each other to the extent that relaxing some assumptions allows the use of a broader range of data. The usefulness of these changes and their impact on assessing safety compliance for nuclear reactors is shown.     

Describing the Dynamics of Attention to TV Commercials: A Hierarchical Bayes Analysis of the Time to Zap an Ad

This paper provides insights into the dynamics of attention to TV commercials via an analysis of the length of time that commercials are viewed before being 'zapped'. The model, which incorporates a flexible baseline hazard rate and captures unobserved heterogeneity across both consumers and commercials using a hierarchical Bayes approach, is estimated on two datasets in which commercial viewing is captured by a passive online device that continually monitors a household's TV viewing. Consistent with previous findings in psychology about the nature of attentional engagement in TV viewing, baseline hazard rates are found to be non-monotonic. In addition, the data show considerable ad-to-ad and household-to-household heterogeneity in zapping behavior. While one of the datasets contains some information on characteristics of the ads, these data do not reveal any firm links between the ad heterogeneity and the ad characteristics. A number of methodological and computational issues arise in the hierarchical Bayes analysis.     

Linear Discriminant Analysis of Multivariate Spatial–Temporal Regressions

Abstract.  We consider classification of the realization of a multivariate spatial–temporal Gaussian random field into one of two populations with different regression mean models and factorized covariance matrices. Unknown means and common feature vector covariance matrix are estimated from training samples with observations correlated in space and time, assuming spatial–temporal correlations to be known. We present the first-order asymptotic expansion of the expected error rate associated with a linear plug-in discriminant function. Our results are applied to ecological data collected from the Lithuanian Economic Zone in the Baltic Sea.     

Empirical Bayes estimation of parameters in Markov transition probability matrix with computational methods

Empirical Bayes estimator for the transition probability matrix is worked out in the cases where we have belief regarding the parameters, For example, where the states seem to be equal or not. In both cases, priors are in accordance with our beliefs. Using EM algorithm, computational methods for different hyperparameters of the empirical Bayes are described. Also, robustness of empirical Bayes procedure is investigated.     

Wavelet Shrinkage with Double Weibull Prior

In this article, we propose a denoising methodology in the wavelet domain based on a Bayesian hierarchical model using Double Weibull prior. We propose two estimators, one based on posterior mean (Double Weibull Wavelet Shrinker, DWWS) and the other based on larger posterior mode (DWWS-LPM), and show how to calculate them efficiently. Traditionally, mixture priors have been used for modeling sparse wavelet coefficients. The interesting feature of this article is the use of non-mixture prior. We show that the methodology provides good denoising performance, comparable even to state-of-the-art methods that use mixture priors and empirical Bayes setting of hyperparameters, which is demonstrated by extensive simulations on standardly used test functions. An application to real-word dataset is also considered.     

影响我国劳动力就业的因素分析及政策选择

当前受劳动力市场中的诸多矛盾及体制性弊端的影响,我国的劳动力就业面临着内部阻力和外部压力,劳动力的就业问题成为影响我国经济发展的突出问题.要妥善解决这一问题,就要求我们实施积极的就业政策,通过创造就业机会、转换就业结构、深化劳动就业体制改革、提高劳动者素质等措施,从根本上提高我国劳动力的就业能力,从而促进我国劳动力的顺利就业与再就业.     

要素跨国流动与比较优势变形:实证与理论拓展

比较优势理论是国际贸易理论的基石。通过考察"金砖国家"外资流入与显性比较优势指数变化,发现两者存在正相关性。在全球化经济下,对比较优势的把握应深入到要素层面,即要素质量、数量和要素组合共同决定比较优势。要素跨国流动对要素价格、质量、组合以及规模经济形成产生影响进而影响比较优势,由此,中国应立足提升要素质量的规划,参与全球化经济。     

“江蚁”抗逆力的实证研究

“蚁族”是对高校毕业生低收入聚居群体的典型概括,被视为继农民、农民工、下岗工人之后的第四大弱势群体.本文采用实证主义研究方法,运用自编量表对“江蚁”的抗逆力水平进行测量与分析,得出结论:“江蚁”普遍面临着生存压力,其中三成缺乏必要的抗逆力；外在保护因素严重不足,自我效能感(自信心)与自知力(自我认同度)偏低是影响抗逆力...     

Estimation in an empirical bayes model for longitudinal and cross‐sectionally clustered binary data

Some studies generate data that can be grouped into clusters in more than one way. Consider for instance a smoking prevention study in which responses on smoking status are collected over several years in a cohort of students from a number of different schools. This yields longitudinal data, also cross‐sectionaliy clustered in schools. The authors present a model for analyzing binary data of this type, combining generalized estimating equations and estimation of random effects to address the longitudinal and cross‐sectional dependence, respectively. The estimation procedure for this model is discussed, as are the results of a simulation study used to investigate the properties of its estimates. An illustration using data from a smoking prevention trial is given.