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.

A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances / D'Ambrosio, Antonio; Heiser, W. J.. - In: PSYCHOMETRIKA. - ISSN 0033-3123. - 81:3(2016), pp. 774-794. [10.1007/s11336-016-9505-1]

A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances

D'AMBROSIO, ANTONIO;
2016

Abstract

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.
2016
A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances / D'Ambrosio, Antonio; Heiser, W. J.. - In: PSYCHOMETRIKA. - ISSN 0033-3123. - 81:3(2016), pp. 774-794. [10.1007/s11336-016-9505-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/634177
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