Analysis of longitudinal count data in long sequences

长序列纵向计数数据分析

• 批准号: 9150161
• 负责人: Paul Albert
• 金额: $ 4.48万
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9150161
• 关键词: Accounting; Address; Automobile Driving; Data; Data Analyses; Methods; Modeling; Performance; Procedures; Research; Source; Statistical Methods; Techniques; base; longitudinal analysis; teen driving

We have developed a method for marginal analysis of the driving data that motivated this research. The method can be used to deal with correlation within subjects effectively. Two sources of within-subject correlation are considered: variability between subjects and serial correlation. We propose to address the variability between subjects by explicitly adjusting for subject as a fixed effect. To deal with serial correlation, we propose to use separated blocks, with a block-diagonal correlation matrix to account for as much correlation as possible within blocks, and a separation of suitable size to control the correlation between blocks. This procedure may be repeated randomly and the results will be synthesized using the multiple outputation technique. Model-based inference may be used to enhance the performance of the method, especially when the robust variance estimate performs poorly.

我们开发了一种对驾驶数据进行边际分析的方法，从而推动了这项研究。该方法可以有效地处理对象内部的相关性。考虑受试者内相关性的两个来源：受试者之间的变异性和序列相关性。我们建议通过明确调整受试者作为固定效应来解决受试者之间的变异性。为了处理序列相关性，我们建议使用分离的块，使用块对角相关矩阵来解释块内尽可能多的相关性，并使用适当大小的分离来控制块之间的相关性。该过程可以随机重复，并且将使用多重输出技术来合成结果。基于模型的推理可用于增强该方法的性能，尤其是当鲁棒方差估计表现不佳时。