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

协作研究：集群纵向数据的混合总体平均和个体特定模型

• 批准号: 0087179
• 负责人: Mark Handcock
• 金额: $ 2.35万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-01-01 至 2002-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0087179&HistoricalAwards=false
• 关键词: Collaborative; Research; Hybrid; Population; Average

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.

这个与Scott（0088061）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聚类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。 该扩展将纳入Scott和汉考克（1998）开发的一组简约模型，以确定个体特异性差异中的群体水平模式。 该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。 将开发用于评估模型有效性的推理，以及信息标准和贝叶斯因子方法。 这项工作的另一个成果是扩展Splus中的lme软件，以纳入这些新方法（平埃罗和贝茨，1995年）。 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，研究结果对工资不平等和劳动力市场分割的影响将被探讨并发表在应用期刊上，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，目标是了解人口中异质性的结构，并在这样做的过程中产生对社会现象的洞察。 在纵向研究中，随着时间的推移，受试者的差异越来越大，受试者中自然分组或集群的识别具有重要的影响。 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的反应内的结构的规格。 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。 这项研究将扩展Banfield和Raftery（1993）开发的聚类纵向数据的统计模型，以包括一个新的，简约的，可解释的个人行为表示。 这种扩展代表了一种混合的群体平均和个体特异性的方法来建模的异质性在个人档案。