Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

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 modeling and visualization 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.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.