Mixtures of weighted distance-based models for ranking data with applications in political studies

Paul H. LEE, Leung Ho Philip YU

Research output: Contribution to journalArticles

26 Citations (Scopus)

Abstract

Analysis of ranking data is often required in various fields of study, for example politics, market research and psychology. Over the years, many statistical models for ranking data have been developed. Among them, distance-based ranking models postulate that the probability of observing a ranking of items depends on the distance between the observed ranking and a modal ranking. The closer to the modal ranking, the higher the ranking probability is. However, such a model assumes a homogeneous population, and the single dispersion parameter in the model may not be able to describe the data well. To overcome these limitations, we formulate more flexible models by considering the recently developed weighted distance-based models which can allow different weights for different ranks. The assumption of a homogeneous population can be relaxed by an extension to mixtures of weighted distance-based models. The properties of weighted distance-based models are also discussed. We carry out simulations to test the performance of our parameter estimation and model selection procedures. Finally, we apply the proposed methodology to analyze synthetic ranking datasets and a real world ranking dataset about political goals priority. Copyright © 2012 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)2486-2500
JournalComputational Statistics and Data Analysis
Volume56
Issue number8
Early online dateFeb 2012
DOIs
Publication statusPublished - Aug 2012

Citation

Lee, P. H., & Yu, P. L. H. (2012). Mixtures of weighted distance-based models for ranking data with applications in political studies. Computational Statistics and Data Analysis, 56(8), 2486-2500. doi: 10.1016/j.csda.2012.02.002

Keywords

  • Ranking data
  • Distance-based models
  • Mixtures models

Fingerprint Dive into the research topics of 'Mixtures of weighted distance-based models for ranking data with applications in political studies'. Together they form a unique fingerprint.