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首页> 外文学位 >Multilevel models for intensive longitudinal data with heterogeneous error structure: Covariance transformation and variance function models.
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Multilevel models for intensive longitudinal data with heterogeneous error structure: Covariance transformation and variance function models.

机译：具有异构错误结构的密集纵向数据的多级模型：协方差转换和方差函数模型。

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摘要

Recent developments in data collection methods in the behavioral and social sciences, such as Ecological Momentary Assessment (EMA) enables researchers to gather intensive longitudinal data (ILD) and to examine more detailed features of intraindividual variation of a variable(s) over time. Due to its high intensity of assessments within individuals, ILD often has different characteristics from traditional longitudinal data with a few measurement occasions and requires different assumptions of statistical models in use. In the present thesis, issues in the analysis of ILD and problems of current use of statistical models for the analysis of ILD are discussed and investigated. Specifically, the issue of heterogeneity of autocorrelation and variance across individuals in ILD is extensively studied for multilevel models (MLMs). In chapter 2, a brief introduction to multilevel models and issues in modeling residual covariance structure in MLMs are provided and discussed. In chapter 3, it is shown that bias in estimation of parameters in MLMs under homogeneity assumption is not ignorable when autocorrelation differs across individuals and its average is high. It is also shown that a transformation method, which multiplies variables in the model by the inverse of Cholesky factor of individual-specific error covariance, attenuates the bias for ILD. Chapter 4 reviews variance function models for heterogeneous variance and introduces a two-step MLM approach for modeling heterogeneous variance using squared residuals. A simulation study showed that the two-step MLM does not suffer from non-convergence and is applicable to ILD.
机译：行为和社会科学中数据收集方法的最新发展，例如生态矩评估（EMA），使研究人员能够收集密集的纵向数据（ILD）并检查随时间变化的变量的个体差异的更详细特征。由于其在个人内部的评估强度很高，ILD经常具有与传统纵向数据不同的特征，并且具有少数测量场合，并且需要使用不同的统计模型假设。本文讨论并研究了ILD分析中的问题和当前使用ILD分析的统计模型的问题。具体来说，ILD中个体之间自相关和方差的异质性问题已针对多层模型（MLM）进行了广泛研究。在第二章中，简要介绍了多层模型以及在MLM中建模剩余协方差结构的问题。在第三章中，表明了当个体之间的自相关不同并且平均值较高时，在均质假设下的MLM参数估计中的偏差是不可忽略的。还表明，将模型中的变量乘以特定于个体的误差协方差的Cholesky因子的倒数的变换方法会减弱ILD的偏差。第4章回顾了用于异质方差的方差函数模型，并介绍了一种两步MLM方法，用于使用平方残差建模异质方差。仿真研究表明，两步式传销不会遭受不收敛现象，适用于ILD。

著录项

  • 作者

    Jahng, Seungmin.;

  • 作者单位

    University of Missouri - Columbia.;

  • 授予单位 University of Missouri - Columbia.;
  • 学科 Quantitative psychology.;Biostatistics.;Statistics.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 89 p.
  • 总页数 89
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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