Winter Workshop on Longitudinal Data Analysis

纵向数据分析冬季研讨会

• 批准号: 0502454
• 负责人: Linda Young
• 金额: --
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-01 至 2005-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0502454&HistoricalAwards=false
• 关键词: Winter; Workshop; Longitudinal; Data; Analysis; Winter; Workshop; Longitudinal; Data; Analysis

Longitudinal data are the result of taking repeated measurements on experimental units over time. Because scientific interest often lies in how a response changes over time, these data arise in both observational and experimental studies in a wide variety of fields. Creative solutions to difficult theoretical issues in longitudinal data analysis often rely on computationally intensive methods. A workshop on longitudinal methods appeals to a wide spectrum of researchers with diverse statistical interests ranging from clinical trials to social behavior to environmental assessment. The challenge of drawing inferences based on diverse datasets attracts those interested in theoretical and methodological statistics. Finally, the excitement of accepting the challenge of analyzing unorthodox data where existing statistical methodology is not satisfactory will undoubtedly fascinate researchers concerned with applications of statistics. This workshop will provide a forum for the positive interaction between theoretical developments and advances in practical applications of longitudinal methods. The specific objectives of the workshop on longitudinal data analysis, and consequently its potential broader impacts, are to generate interest in this topic among researchers nationwide (particularly young researchers) and among faculty and graduate students at the University of Florida and neighboring universities; to encourage future collaborations among researchers; and to promote advances in both the theory and application of longitudinal data.

纵向数据是随着时间的推移对实验单元进行重复测量的结果。 由于科学兴趣通常在于反应随时间变化的方式，因此这些数据在各种领域的观察和实验研究中都会出现。 纵向数据分析中难以理论问题的创造性解决方案通常取决于计算密集型方法。 纵向方法的研讨会吸引了各种各样的统计兴趣研究人员，从临床试验到社会行为再到环境评估。 根据不同数据集进行推断的挑战吸引了对理论和方法论统计学感兴趣的人。 最后，接受分析现有统计方法不令人满意的非正统数据的挑战的兴奋无疑会使研究人员关注有关统计应用的研究人员。 该研讨会将为理论发展与纵向方法的实际应用的积极互动提供一个论坛。研讨会关于纵向数据分析的特定目标，因此其潜在的广泛影响是在全国研究人员（尤其是年轻的研究人员）以及佛罗里达大学和邻近大学的教职员工和研究生中引起对这一主题的兴趣；鼓励研究人员未来的合作；并促进纵向数据理论和应用的进步。