The researcher collecting hierarchical data is frequently confronted with incompleteness. Since the processes governing missingness are often outside the investigator's control, no matter how well the experiment has been designed, careful attention is needed when analyzing such data.We sketch a standard framework and taxonomy largely based on Rubin's work. After briefly touching upon (overly) s...

Survey non-response is an important problem in statistics, economics and social sciences. The paper reviews the missing data framework of Little & Rubin [Little and Rubin, 1986]. It presents a survey of techniques to deal with non-response in surveys using a likelihood based approach. The focuses on the case where the probability of a data missing depends on its value. The paper uses the two-st...

To combine ordered data originating from multiple sources, one needs a framework that can represent uncertainty about the possible orderings or, as we call it, order-incomplete data. Examples of such data are lists of properties (such as hotels and restaurants) ranked by an unknown function reflecting relevance or customer ratings; documents edited concurrently with uncertainty on the order of ...

Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives|the likelihood-based and the Bayesian. The goal is twofold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorith...