nips nips2007 nips2007-142 nips2007-142-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Guy Lebanon, Yi Mao
Abstract: 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. 1
[1] D. E. Critchlow. Metric Methods for Analyzing Partially Ranked Data. Springer, 1986.
[2] M. A. Fligner and J. S. Verducci. Distance based ranking models. Journal of the Royal Statistical Society B, 43:359–369, 1986.
[3] M. G. Kendall. A new measure of rank correlation. Biometrika, 30, 1938.
[4] G. Lebanon and J. Lafferty. Conditional models on the ranking poset. In Advances in Neural Information Processing Systems, 15, 2003.
[5] C. L. Mallows. Non-null ranking models. Biometrika, 44:114–130, 1957.
[6] J. I. Marden. Analyzing and modeling rank data. CRC Press, 1996.
[7] R. P. Stanley. Enumerative Combinatorics, volume 1. Cambridge University Press, 2000. 8