Inference from Nonrandomly Missing Categorical Data: An Example from a Genetic Study on Turner's Syndrome
研究了分类数据非随机缺失时的推断方法，利用最大似然估计总体比例，并推荐结合缺失机制参数的敏感性分析，以特纳综合征遗传数据为例。
Abstract Abstract The process leading to partial classification with categorical data is sometimes nonrandom. A particular model accounting for incomplete data, which allows the probability of uncertain classification to depend on category identity, is utilized for an analysis of data obtained from a genetic study on Turner's syndrome. Estimates of population proportions are obtained from maximum likelihood. A method for handling nonrandomly missing data arrayed in contingency tables is discussed. Sensitivity analyses incorporating parameters related to the missing-data mechanism are recommended for estimation and testing. Key Words: Contingency tablesMissing dataNon-randomnessPartial classificationSensitivity analysisTurner's syndrome