Non-Parametric Modeling of Partially Ranked Data
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 computationally 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. A bias-variance analysis and an experimental study demonstrate the applicability of the proposed method.
Speaker Bio
Guy Lebanon is an assistant professor at Purdue University with a joint appointment in Statistics and Electrical and Computer Engineering. His research area includes machine learning and computational statistics with a particular emphasis on modeling text documents and partially ranked data.
Prof. Lebanon received the 2007 Teaching for Tomorrow Award from Purdue University and the Best Presentation Award in the 2004 LTI Student Research Symposium. Prof. Lebanon received his PhD in 2005 from the School of Computer Science, Carnegie Mellon University and MS and BA degrees in Computer Science from Technion - Israel Institute of Technology.