Non-parametric Modeling of Partially Ranked Data - Lebanon, Guy; Mao, Yi (Advances in Neural Information Processing Systems, 2007, 2007)
Statistical models on full and partial rankings of n items are often of limited practical use for large n due to computational consideration. We explore the use of non-parametric models for partially ranked data and derive efﬁcient 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 ﬁrst time a non-parametric coherent and consistent model capable of efﬁciently aggregating partially ranked data of different types.