Collaborative Research: Hybrid Population-Average and Individual-Specific Models for Clustered Longitudinal Data

这个与Handcock（0087179）的合作项目将开发一类纵向数据模型，适用于对此类数据中的集群和异构模式进行建模。这项工作扩展了Banfield和Raftery（1993）的建模范式，其中通过显式统计模型识别集群。这一扩展将纳入斯科特和汉考克（1998）开发的一个精简的模型类别，以确定个体特定差异中的种群水平模式。本研究将开发算法来估计模型，并将结合Bensmail等人（1997）的方法来建立贝叶斯模型公式。评估模型有效性的推理将被开发，信息准则和贝叶斯因子方法也将被开发。这项工作的进一步成果将是扩展Splus中的lme软件以纳入这些新方法（Pinheiro和Bates 1995）。该项目的一个重要部分将是对工资不平等的长期趋势进行案例研究。这将包括对两个重要经济时期的比较，并将新模型下的发现与更传统的混合效应模型的结果进行对比。此外，研究结果对工资不平等和劳动力市场细分的影响将在应用期刊上进行探讨和发表，为主题研究人员提供这种方法的桥梁。在许多社会、行为和生物统计研究中，目标是了解人口异质性的结构，并由此产生对社会现象的洞察力。在纵向研究中，随着时间的推移，研究对象可能变得越来越分化，在研究对象中确定自然分组或集群具有重要的意义。它们可能会提供证据，表明存在几种不同的现象——也就是说，不同的主体可能存在不同的社会过程。这些集群的准确识别取决于受试者反应中的结构规范。描述个人资料这方面的方法很少，而且往往难以实质性地解释。本研究将扩展Banfield和Raftery（1993）开发的聚类纵向数据统计模型，以包括一个新的、简洁的、可解释的个人行为表示。这个扩展代表了一种混合的种群平均和个体特定的方法来建模个体配置文件中的异质性。

This collaborative project with Handcock (0087179) will develop a class of longitudinal data models appropriate for modeling clustering and heterogeneous patterns in such data. This work extends the modeling paradigm of Banfield and Raftery (1993), in which clusters are identified via an explicit statistical model. The extension will incorporate a parsimonious class of models developed by Scott and Hancock (1998) to identify population-level patterns in individual-specific differences. The study will develop algorithms to estimate the models, and will incorporate the approach of Bensmail et al. (1997) to establish a Bayesian model formulation. Inference to assess the validity of the models will be developed, as will information criterion, and Bayes factor approaches. A further outcome of this work will be an extension of the lme software in Splus to incorporate these new methods (Pinheiro and Bates 1995). An important part of the project will be the development of a case study of long-term trends in wage inequality. This will include comparisons of two important economic periods and will contrast the findings under the new models to results from more traditional mixed effects models. In addition, the implications of the findings for wage inequality and labor market segmentation will be explored and published in an applied journal to provide a bridge for subject matter researchers to this methodology.In much of social, behavioral and biostatistical research, the goal is to understand the structure of the heterogeneity in a population, and in so doing yield insight into social phenomena. In longitudinal studies subjects can become increasingly differentiated over time, and the identification of natural groupings, or clusters, in the subjects has important consequences. They may yield evidence suggesting the presence of several distinct phenomena--that is, different social processes could exist for different subjects. The accurate identification of these clusters depends on the specification of the structure within a subject's responses. Methods that describe this aspect of an individual's profile have been sparse and often hard to interpret substantively. This research will extend the statistical models for clustered longitudinal data developed in Banfield and Raftery (1993) to include a new, parsimonious, and interpretable representation of individual behavior. This extension represents a hybrid population-average and individual-specific approach to modeling the heterogeneity in individual profiles.