In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.

中文翻译：

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非随机粗略观测值分类回归分析中的可靠推论

在大多数调查中，人们会遇到数据丢失或更普遍的情况。处理这些数据的传统方法需要强有力的，不可检验的且常常令人怀疑的假设，例如，随机地进行粗化。但是由于由此产生的潜在的严重偏差，人们对仅包含有关粗化过程的可靠知识的方法的兴趣日益浓厚，从而导致结果不精确但可靠。本着这种精神，我们研究了回归分析，使用了一个粗糙的分类相关变量和精确观察到的分类协变量。我们的（概貌）基于可能性的方法可以吸收有关粗化过程的薄弱知识，从而提供了传统方法和谨慎策略的综合，避免了任何粗化假设。这也允许讨论有关粗化过程的不确定性，除了采样不确定性和模型不确定性。就业研究所进行的小组研究“劳动力市场与社会保障”的数据说明了我们的程序，其问卷设计得出了粗略的数据。