| 题名 |
Analysis of Covariance Structures in Time Series |
| DOI |
10.6339/JDS.2008.06(4).432 |
| 作者 |
Jennifer S. K. Chan；Boris S. T. Choy |
| 关键词 |
Longitudinal data ； robustness ； serial correlation |
| 期刊名称 |
Journal of Data Science |
| 卷期/出版年月 |
6卷4期（2008 / 10 / 01） |
| 页次 |
573 - 589 |
| 内容语文 |
英文 |
| 英文摘要 |
Longitudinal data often arise in clinical trials when measurements are taken from subjects repeatedly over time so that data from each subject are serially correlated. In this paper, we seek some covariance matrices that make the regression parameter estimates robust to misspecification of the true dependency structure between observations. Moreover, we study how this choice of robust covariance matrices is affected by factors such as the length of the time series and the strength of the serial correlation. We perform simulation studies for data consisting of relatively short (N=3), medium (N=6) and long time series (N=14) respectively. Finally, we give suggestions on the choice of robust covariance matrices under different situations. |
| 主题分类 |
基礎與應用科學 >
資訊科學 基礎與應用科學 > 統計 |
| 被引用次数 |
