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。
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。
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。
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。
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。
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。
Redline offered one additional hypothesis, pointing out that limited longitudinal data indicate that overall sleep times have been declining, by one or more hours, over the past 20 to 30 years.
Redline提出了另一个假设，指出有限的纵向数据提示在过去的20- 30年中，全部的睡眠时间已经减少了一小时或者更多。
We estimated the time at which the rate of decline begins to accelerate (the change point), and the pre - and post-acceleration rates of decline, from the longitudinal data using a change point model.
利用改变点模型从纵向数据中找出加速点、前加速期下降速率和后加速期下降速率。
Longitudinal data is referred to data in which individuals are measured repeatedly through time, so it combines elements of cross-sectional data and time-series data.
纵向数据是指对每个个体在不同时间进行观测而得到的由截面和时间序列融合在一起的数据。
Longitudinal data is typical related data, and it is desirable to choose correct data variance structure since the correct structure affects the efficiency of estimation.
纵向数据为典型的相关数据，数据方差结构选择的正确与否直接影响估计的效率。
Longitudinal data is typical related data, and it is desirable to choose correct data variance structure since the correct structure affects the efficiency of estimation.
纵向数据为典型的相关数据，数据方差结构选择的正确与否直接影响估计的效率。
应用推荐