613 – Bootstrapping, Resampling, and Ranking
Is There a Partial Consensus Ordering Between Rankings?
Sponsor: Section on Statistical Learning and Data Mining
Keywords: partial rankings, top-K rank list, rank aggregation, multistage model, maximum likelihood estimation, consensus
Srinath Sampath
The Ohio State University
Joseph Verducci
The Ohio State University
We propose an innovative approach to the problem recently posed by Hall and Schimek (2012): determining at what point the agreement between two rankings of a long list of items degenerates into noise. We modify the method of estimation in Fligner and Verducci's (1988) multistage model for rankings, from maximum likelihood of conditional agreement over a sample of rankings to a locally smooth estimator of agreement. Through simulations we show that this innovation performs very well under several conditions. Some ramifications are discussed as planned extensions.
