Robust Missing Data Methods for Categorical Regression

描述(由申请人提供)：在数据缺失和模型错误指定的情况下，从分类回归模型中获得稳健的统计推断的改进方法将是流行病学和卫生保健研究界的宝贵工具。目前，流行病学模型通常用于确定酒精相关症状的模式，定义酒精使用障碍的标准，并评估管理酒精饮料使用和分销的政策。这类模型经常依赖于包含不完整数据的数据集。虽然商业上可用的统计软件提供了一些自动的缺失值程序(例如，数据补偿、期望最大化)，但还需要进一步的理论和实证研究，以开发更可靠的统计方法。 Martingale Research将开发结合随机估计、渐近统计和最大似然重新编码的最新进展的稳健期望最大化(REM)算法，以实现以下目标：1)设计和实现适合于流行病学问题的分类回归建模的REM算法；2)从理论和经验上研究REM算法在存在缺失数据和模型错误指定的情况下的性能；以及3)扩展REM算法，以在存在缺失数据和模型错误指定的情况下最优地估计预处理转换。这些结果将证明进一步进行第二阶段调查所需的基本技术可行性，并为开发商用软件奠定基础。

DESCRIPTION (provided by applicant): Improved methods for obtaining robust statistical inferences from categorical regression models in the presence of missing data and model misspecification would be an invaluable tool to the epidemiological and health care research communities. Presently epidemiological models are typically designed to identify patterns of alcohol-related symptoms, define criteria of alcohol use disorders, and evaluate policies regulating use and distribution of alcoholic beverages. Such models frequently rely on datasets that contain incomplete-data. While commercially available statistical software provides some automated missing value procedures (e.g., data imputation, Expectation-Maximization), further theoretical and empirical research is required to develop more robust statistical methods. Martingale Research will develop Robust Expectation-Maximization (REM) algorithms that combine recent advances in stochastic estimation, asymptotic statistics, and maximum likelihood recoding to achieve the following objectives: 1) design and implement REM algorithms that are suited to categorical regression modeling for epidemiological problems, 2) theoretically and empirically investigate REM algorithm performance in the presence of missing data and model misspecification and 3) extend the REM algorithms to optimally estimate preprocessing transformations in the presence of missing data and model misspecification. These results will demonstrate the essential technical feasibility required for further Phase II investigations and provide the foundation for developing commercially available software.