Mixtures of weighted distance-based models for ranking data with applications in political studies
Mixtures of weighted distance-based models for ranking data with applications in political studies
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.
Distance-based models, Mixtures models, Ranking data
2486-2500
Lee, Paul H.
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Yu, Philip L.H.
67db467c-4f19-4c55-8ad9-0c13faeb15d6
1 August 2012
Lee, Paul H.
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Yu, Philip L.H.
67db467c-4f19-4c55-8ad9-0c13faeb15d6
Lee, Paul H. and Yu, Philip L.H.
(2012)
Mixtures of weighted distance-based models for ranking data with applications in political studies.
Computational Statistics and Data Analysis, 56 (8), .
(doi:10.1016/j.csda.2012.02.002).
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.
This record has no associated files available for download.
More information
Accepted/In Press date: 2 February 2012
Published date: 1 August 2012
Additional Information:
Funding Information:
The research of Philip L. H. Yu was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7473/05H). We thank the associate editor and three anonymous referees for their helpful suggestions for improving this article.
Keywords:
Distance-based models, Mixtures models, Ranking data
Identifiers
Local EPrints ID: 479924
URI: http://eprints.soton.ac.uk/id/eprint/479924
ISSN: 0167-9473
PURE UUID: c39f7df1-41af-49d9-b19a-e321ae34581d
Catalogue record
Date deposited: 28 Jul 2023 16:53
Last modified: 18 Mar 2024 04:09
Export record
Altmetrics
Contributors
Author:
Paul H. Lee
Author:
Philip L.H. Yu
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics