International Statistical Review

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

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  • Author(s): Julia Plass, Marco E.G.V. Cattaneo, Thomas Augustin, Georg Schollmeyer, Christian Heumann
  • Article first published online: 07 Jun 2019
  • DOI: 10.1111/insr.12329
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Summary 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.

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