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1·In longitudinal data analysis, homogeneity of variance is a basic assumption.

在纵向数据分析中，模型方差的齐性是一个基本假定。

2·The linear mixed model for longitudinal data proposed by Laird and Ware is studied.

考虑纵向数据的一般线性混合效应模型。

3·There exist few longitudinal data on the predictive role of CIMT in the occurrence of carotid plaque.

目前很少有关于颈动脉斑块的发生在CIMT中的预测作用的纵向数据。

4·In this paper, we consider the diagnostics for the mean and covariance parameters in longitudinal data model.

本文主要研究了纵向数据模型的均值参数和方差参数的统计诊断问题。

5·The prominent advantage of longitudinal data is that it can analyze effectively the change of individuals over time.

纵向数据的最大优点就是它将截面数据和时间序列数据结合在一起，更好地分析出个体随时间的变化趋势。

6·Conclusion: Multivariate and multilevel models can effectively analyze longitudinal data with several dependent variables.

结论：多元多层模型可有效地分析具有多个因变量的纵向研究资料。

7·It is necessary to make a reasonable covariance structure for longitudinal data so as to fit these data with linear or nonlinear models.

为了用线性或非线性模型拟合纵向数据，需要确定一个合理的协方差结构。

8·Chapter 2 studies the tests for heteroscedasticity and correlation in longitudinal data model with uniform correlation covariance structure.

第二章系统讨论了具有一致相关的纵向数据模型中异方差和相关性的检验问题。

9·Limitations of this study are: first, fewer longitudinal data collection (year), resulting in statistical results would produce a certain deviation.

本文研究的局限是：首先，纵向数据采集的较少(年份)，造成统计结果会产生一定的偏差。

10·We first develop the tests for varying dispersion in two special generalized nonlinear models of longitudinal data: (1) logistic nonlinear models in binomial data;

首先刻画了非线性随机效应模型的异方差类型，进而研究了非线性随机效应模型的异方差检验；