Robust Missing Data Methods for Categorical Regression

描述（由申请人提供）：在存在数据缺失和模型错误规范的情况下，从分类回归模型中获得稳健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，确定酒精使用障碍的标准，并评估管制酒精饮料使用和分配的政策。这种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化的缺失值程序（例如，数据输入，期望最大化），但需要进一步的理论和实证研究来开发更可靠的统计方法。

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.