Non-parametric Modeling of Partially Ranked Data

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Guy Lebanon, Yi Mao


Statistical models on full and partial rankings of n items are often of limited prac- tical use for large n due to computational consideration. We explore the use of non-parametric models for partially ranked data and derive ef(cid:2)cient procedures for their use for large n. The derivations are largely possible through combinatorial and algebraic manipulations based on the lattice of partial rankings. In particular, we demonstrate for the (cid:2)rst time a non-parametric coherent and consistent model capable of ef(cid:2)ciently aggregating partially ranked data of different types.