Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.     

Semiparametric Bayesian classification with longitudinal markers

Summary.  We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.     

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.     

工业经济增长集聚弹性研究——对中国三大经济圈的实证分析

产业集群理论正在继梯度转移理论、增长极发展理论后成为主导区域经济发展新型区域发展理论。在中国的工业化过程中，珠三角、长三角和环渤海地区随着其地区一体化水平的不断提高制造业都表现为不同程度的产业集聚，其制造业集聚程度与地域经济发展特别是工业经济增长都具有较强的相关性。因此，结合面板数据（panel data）模型的计量方法对中国三大经济圈的工业增长集聚弹性进行估计并就三大经济圈的增长集聚弹性差异的成因进行了分析是十分必要的。     

Cox模型和寿险退保研究中的比例危险性

利用生存分析研究寿险退保问题是一个很好的工具，因为可以将寿险保单的持续期（persistency duration）视为生存期长，而将保单的退保或失效看作一个“保单生命”的结束，这其中的保单退保或失效就成为生存研究的目标事件。而导致保单失效的因素会有很多，只有通过利用Cox比例危险模型拟合寿险退保数据以分析影响客户退保的原因，并在对Cox模型的比例危险假设进行检验时，发现部分影响因素并不遵守此前提条件，从而推理得到这些影响因素在不同的时间段对客户退保的影响方式不同。也就是说，其影响有短期效应和长期效应之分。     

微观计量经济学中的实验设计法——期望“计量经济（学）设计与分析”的出现

 摘　　要:本文论述了实验设计思想在微观计量经济分析中的重要作用。从实验设计三大原理局部控制、随机化和重复出发，重新诠释微观计量经济学（特别是“因果链”分析）中有关方法论的问题，预期实验经济学和微观计量经济学将通过实验设计的思想方法而互相促进，并期望“计量经济（学）设计与分析”一类读物的出现。     

中国区域经济差异源于FDI？——析二次统计误差的形成

 摘　　要:本文比较分析了两项关于FDI与中国区域经济发展差异的研究成果，通过分析这两项成果间迥异的研究结论和相应的近乎对立的政策主张，剖析了实证研究中可能形成的二次统计误差，提请人们从变量选择、模型方法、数据运用等各个方面，全面关注实证研究的客观性和科学性。     

局部平稳过程、稳定过程及GARCH模型的比较研究

本文在Granger（2005）[1]研究成果的基础上对局部平稳过程的大样本性质进行了深入探讨,发现了一些颇具实际价值的理论结果,弥补了Granger（2005）仅利用预测效果标准得到金融数据生成过程的不足,进一步给出了适合于实证分析的判断金融数据生成过程的标准,并在此基础上详细讨论了局部平稳过程、稳定过程及GARCH模型在大样本情况下的区别。本文运用研究得到的结果,在非平稳框架下对中国股票市场上证180指数进行了分析,发现上证180指数收益率具有明显的非平稳特性,并在此基础上进一步讨论了中国股票市场的市场有效性问题。     

基于二值响应模型的房地产泡沫预警方法研究

 本文在总结国内外房地产泡沫预警研究的基础上，论述了二值响应模型在房地产泡沫预警研究中的应用，并以日本为例进行了实证研究。结果表明，二值响应模型在房地产泡沫预警中有比较准确的预测作用。另外，与徐滇庆(2000)的研究不同，有关股市价值的相关变量并不能对房地产泡沫起到明显的预警作用。