Diagnosing Statistical Models for Longitudinal and Family Data

诊断纵向和家庭数据的统计模型

• 批准号: 0550988
• 负责人: Hongtu Zhu
• 金额: $ 13.5万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2006-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0550988&HistoricalAwards=false
• 关键词: Diagnosing; Statistical; Models; Longitudinal; Family

Due to recent computational and theoretical advances, researchers now can analyze longitudinal and family data by using sophisticated parametric and semiparametric models. Effective application of these models, however, has been hindered by the lack of well-developed diagnostic tools for testing the validity of their assumptions. Scientists have thus faced the prospect of interpreting results from models that have not been validated adequately. The aim of this project is to develop, evaluate, and implement new diagnostic tools for checking assumptions of parametric and semiparametric models for longitudinal and family data, with a specific focus on improving inferences about covariance structure of these models and their goodness-of-fit to the data sets to which they are applied. The project specifically aims to develop: a local influence approach; new first-order and second-order residual diagnostics for assessing mean and covariance structure of parametric and semiparametric models; diagnostic tools for assessing empirical likelihood; and score test statistics for selecting random-effects components and for testing parametric functions in semiparametric models. As these methods are developed, they will be evaluated and refined through extensive Monte Carlo simulations and data analysis. The efficacy of the tools developed under this award will be demonstrated by applying them to two data sets from longitudinal family studies. Software programs for implementing all of these tools, once tested, will be made available to the public via the internet. This project will provide much needed data-mining tools for application in longitudinal and family studies. These statistical tools will help scientists to choose an "appropriate" model for the data of a given study, thus maximizing the likelihood of drawing correct scientific conclusions. The companion software package will allow these tools to be disseminated widely and applied to a broad range of populations under study within the behavioral, social, and economic sciences. This project will apply these new methods to two large databases to address problems of public health and social importance. Results from these applications will show our methods to be useful tools for studying a wide range of issues, including: identification of risk factors for cancer, assessment of the impact of substance use and environmental factors on the neuropsychological development of children, examination of factors associated with the lifespan of system components, and identification of candidate genes for nicotin dependence and its comorbidity with alcohol use and psychiatric disorders.

由于最近的计算和理论进步，研究人员现在可以使用复杂的参数和半参数模型来分析纵向和家庭数据。 然而，由于缺乏成熟的诊断工具来测试其假设的有效性，这些模型的有效应用受到了阻碍。 因此，科学家们面临着解释尚未得到充分验证的模型结果的前景。 该项目的目的是开发、评估和实施新的诊断工具，用于检查纵向和家庭数据的参数和半参数模型的假设，特别注重改进对这些模型的协方差结构及其与所应用的数据集的拟合优度的推论。 该项目的具体目标是制定： 本地影响力方法；新的一阶和二阶残差诊断，用于评估参数和半参数模型的均值和协方差结构；用于评估经验可能性的诊断工具；并为选择随机效应分量和测试半参数模型中的参数函数进行评分测试统计。 随着这些方法的开发，将通过广泛的蒙特卡罗模拟和数据分析对其进行评估和完善。 该奖项开发的工具的功效将通过将其应用于纵向家庭研究的两个数据集来证明。 用于实现所有这些工具的软件程序一旦经过测试，将通过互联网向公众提供。该项目将为纵向和家庭研究的应用提供急需的数据挖掘工具。 这些统计工具将帮助科学家为给定研究的数据选择“适当”的模型，从而最大限度地提高得出正确科学结论的可能性。 配套软件包将使这些工具得以广泛传播，并应用于行为、社会和经济科学领域的广泛研究人群。 该项目将把这些新方法应用于两个大型数据库，以解决公共卫生和社会重要性问题。 这些应用的结果将表明我们的方法是研究广泛问题的有用工具，包括：识别癌症的危险因素，评估物质使用和环境因素对儿童神经心理发展的影响，检查与系统组件寿命相关的因素，以及识别尼古丁依赖及其与酒精使用和精神疾病共病的候选基因。