General partially linear varying-coefficient transformation models for ranking data

Abstract

In this paper,we propose a class of general partially linear varying-coefficient transformation models for ranking data. In the models, the functional coefficients are viewed as nuisance parameters and approximated by B-spline smoothing approximation technique. The B-spline coefficients and regression parameters are estimated by rank-based maximum marginal likelihood method. The three-stage Monte Carlo Markov Chain stochastic approximation algorithm based on ranking data is used to compute estimates and the corresponding variances for all the B-spline coefficients and regression parameters. Through three simulation studies and a Hong Kong horse racing data application, the proposed procedure is illustrated to be accurate, stable and practical.

Mixed Membership Models for Rank Data: Investigating Structure in Irish Voting Data

“…Recently, a number of models have been developed that capture underlying group structure for rank data when concomitant information for the voters is also available (Gormley and Murphy, 2008b;Francis et al, 2010;Lee and Yu, 2010;Li et al, 2012). It would be worthwhile to extend the mixed membership modeling framework for rank data to include such concomitant information.…”