Zhang Guangjian, Browne Michael W
a University of Notre Dame.
b The Ohio State University.
Multivariate Behav Res. 2010 May 28;45(3):453-82. doi: 10.1080/00273171.2010.483375.
Dynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the interdependence of successive observations. Bootstrap methods can fill this need, however. The standard bootstrap of individual timepoints is not appropriate because it destroys their order in time and consequently gives incorrect standard error estimates. Two bootstrap procedures that are appropriate for dynamic factor analysis are described. The moving block bootstrap breaks down the original time series into blocks and draws samples of blocks instead of individual timepoints. A parametric bootstrap is essentially a Monte Carlo study in which the population parameters are taken to be estimates obtained from the available sample. These bootstrap procedures are demonstrated using 103 days of affective mood self-ratings from a pregnant woman, 90 days of personality self-ratings from a psychology freshman, and a simulation study.
动态因素分析通过数量少得多的潜在因素组成的时间序列，总结了在一系列明显变量上重复测量所得分数的变化情况。由于连续观测值之间存在相互依存关系，与通常的主体间因素分析相比，参数估计标准误差的代数公式更难获得。不过，自助法可以满足这一需求。对各个时间点进行标准自助抽样并不合适，因为这会破坏它们的时间顺序，从而给出错误的标准误差估计值。本文描述了两种适用于动态因素分析的自助程序。移动块自助法将原始时间序列分解为多个块，并抽取块样本而非单个时间点的样本。参数自助法本质上是一项蒙特卡罗研究，其中总体参数被视为从可用样本中获得的估计值。通过一名孕妇103天的情感状态自评、一名心理学新生90天的性格自评以及一项模拟研究，对这些自助程序进行了演示。
