论文标题
是否随机丢失：半参数测试方法
Missing at Random or Not: A Semiparametric Testing Approach
论文作者
论文摘要
缺少数据的实际问题很常见，并且已经开发了有关统计程序的有效性和效率的统计方法。在中心重点上，人们对管理数据丢失的机制有很长的兴趣，并且正确确定适当的机制对于进行适当的实际研究至关重要。传统概念包括三个普通电位类别 - 完全随机丢失，随机丢失，而不是随机丢失。在本文中，我们提出了一种新的假设测试方法，用于在随机丢失和而不是随机丢失之间做出决定。由于随机缺失的潜在替代方案是广泛的，因此我们将调查重点放在具有仪器变量的一般模型上，用于丢失的数据，而不是随机。我们的设置广泛适用，这要归功于丢失数据的模型是非参数的，不需要明确的模型规范来丢失数据。基本思想是在估计器之间制定适当的差异度量，这些估计量仅在不存在时才丢失时属性有显着差异。我们表明，我们的新假设检验方法在随机或不随机或不丢失之间实现了目标数据的目标。我们通过理论分析，仿真研究和实际数据分析证明了新测试的可行性，有效性和功效。
Practical problems with missing data are common, and statistical methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. The conventional notions include the three common potential classes -- missing completely at random, missing at random, and missing not at random. In this paper, we present a new hypothesis testing approach for deciding between missing at random and missing not at random. Since the potential alternatives of missing at random are broad, we focus our investigation on a general class of models with instrumental variables for data missing not at random. Our setting is broadly applicable, thanks to that the model concerning the missing data is nonparametric, requiring no explicit model specification for the data missingness. The foundational idea is to develop appropriate discrepancy measures between estimators whose properties significantly differ only when missing at random does not hold. We show that our new hypothesis testing approach achieves an objective data oriented choice between missing at random or not. We demonstrate the feasibility, validity, and efficacy of the new test by theoretical analysis, simulation studies, and a real data analysis.
