A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances

Preference rankings usually depend on the characteristics of both the individuals judging a set of objects and the objects being judged. This topic has been handled in the literature with log-linear representations of the generalized Bradley-Terry model and, recently, with distance-based tree models...

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Published in:Psychometrika 2016-09, Vol.81 (3), p.774-794
Main Authors: D’Ambrosio, Antonio, Heiser, Willem J.
Format: Article
Language:English
Subjects:
Assessment
Behavioral Science and Psychology
Busing
Clustering
Consensus
Consumer Behavior
Data Analysis
Generalized linear models
Humanities
Humans
Law
Maximum Likelihood Statistics
Methods
Models, Statistical
Monte Carlo Method
Monte Carlo Methods
Multidimensional Scaling
Preferences
Principal components analysis
Principals
Psychology
Psychometrics
Ratings & rankings
Regression (Statistics)
Statistical Theory and Methods
Statistics for Social Sciences
Testing and Evaluation
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container_title Psychometrika
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Heiser, Willem J.
description Preference rankings usually depend on the characteristics of both the individuals judging a set of objects and the objects being judged. This topic has been handled in the literature with log-linear representations of the generalized Bradley-Terry model and, recently, with distance-based tree models for rankings. A limitation of these approaches is that they only work with full rankings or with a pre-specified pattern governing the presence of ties, and/or they are based on quite strict distributional assumptions. To overcome these limitations, we propose a new prediction tree method for ranking data that is totally distribution-free. It combines Kemeny’s axiomatic approach to define a unique distance between rankings with the CART approach to find a stable prediction tree. Furthermore, our method is not limited by any particular design of the pattern of ties. The method is evaluated in an extensive full-factorial Monte Carlo study with a new simulation design.
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subjects Assessment
Behavioral Science and Psychology
Busing
Clustering
Consensus
Consumer Behavior
Data Analysis
Generalized linear models
Humanities
Humans
Law
Maximum Likelihood Statistics
Methods
Models, Statistical
Monte Carlo Method
Monte Carlo Methods
Multidimensional Scaling
Preferences
Principal components analysis
Principals
Psychology
Psychometrics
Ratings & rankings
Regression (Statistics)
Statistical Theory and Methods
Statistics for Social Sciences
Testing and Evaluation
title A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances
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