Online Program Home
My Program

Abstract Details

Activity Number: 268
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 2:45 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321709
Title: Bayesian Aggregation of Rank Data with Covariates
Author(s): Dingdong Yi* and Xinran Li and Jun S. Liu
Companies: Harvard and Harvard and Harvard
Keywords: rank aggregation ; latent variable model ; PX-DA ; spam ranker detection

The rank aggregation problem is to combine ranking results from different sources in order to generate a "better" ranking list. Existing rank aggregation methods utilize only rank information. In some real applications, however, rank data come with covariates information. Moreover, some rankers are more informative and reliable than others in practice. Therefore, it is desirable to incorporate covariates information and distinguish high quality rankers from spam rankers. Here, we propose the Bayesian aggregation of rank-data with covariates (BARC) and its weighted version BARCW to overcome these limitations. Both BARC and BARCW employ latent variable models, which can incorporate covariates information, for ranked entities. Besides, BARCW quantitatively measures the difference in qualities of rankers. We generate aggregated ranking and its probability interval based on posterior distribution of latent variables estimated using Gibbs sampling with parameter expansion. Simulation studies show that our methods outperform the competing methods in different scenarios. Real-data applications validate our estimation of realiability parameters and effects of covaraiates.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association