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

这个与Scott(0088061)合作的项目将开发一类适用于对此类数据中的集群和异类模式进行建模的纵向数据模型。这项工作扩展了Banfield和Raftery(1993)的建模范式，在该范式中，集群通过显式的统计模型来识别。这一扩展将纳入Scott和Hancock(1998)开发的一类简约型模型，以确定个体特定差异中的种群水平模式。这项研究将开发算法来估计模型，并将纳入本斯梅尔等人的方法。(1997)建立贝叶斯模型公式。将开发评估模型有效性的推理，以及信息标准和贝叶斯因子法。这项工作的另一个结果将是LME软件在sPlus中的扩展，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个案研究。这将包括对两个重要经济时期的比较，并将新模型下的结果与更传统的混合效应模型的结果进行对比。此外，这些发现对工资不平等和劳动力市场分割的影响将被探索并发表在一本应用期刊上，为主题研究人员提供进入这种方法的桥梁。在许多社会、行为和生物统计学研究中，目标是了解人群中异质性的结构，并通过这样做来洞察社会现象。在纵向研究中，受试者可能会随着时间的推移而变得越来越不同，识别受试者中的自然分组或集群具有重要的结果。它们可能会产生证据，表明存在几种不同的现象--即不同的受试者可能存在不同的社会过程。对这些簇的准确识别取决于受试者反应中结构的具体说明。描述个人档案的这一方面的方法一直很少，而且往往很难有实质性的解释。这项研究将扩展Banfield和Raftery(1993)发展的纵向数据集群化的统计模型，以包括对个人行为的新的、简约的和可解释的表示。这一扩展代表了一种混合的总体平均和特定于个人的方法来模拟个人概况中的异质性。

This collaborative project with Scott (0088061) 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.