B1077
Title: Modeling ranking data in a social network
Authors: Jiaqi Gu - The University of Hong Kong (Hong Kong)
Philip Yu - The Education University of Hong Kong (Hong Kong) [presenting]
Abstract: Human interaction and communication have become one of the essential features of social life. Individuals preference behaviors may be influenced from those of their peers or friends in a social network. However, most existing statistical models and methods for ranking data assume independence among the rank-order preferences of different individuals. We introduce a new class of probabilistic models for ranking data in a social network. The new models are able to account for social dependencies among the individuals. An efficient MCMC algorithm is developed for Bayesian inference. Simulation and empirical studies reveal the usefulness of our proposed methods.
