2017
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
ICML 2017poster
We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking i…