Rankings and paired comparisons rankings are ubiquitous in data analysis. Recently, in the literature there has been a growing interest in modeling rank data, especially in trying to explain and/or predict preferences explicitly stated from a sample of judges starting from a set of covariates over a set of alternatives. Both parametric and non-parametric tools have been introduced in order to deal with preference rankings or paired comparison rankings as response variable. In this work we introduce a model dealing with the identification of threshold interaction effects in paired comparisons rankings response data, which integrates recursive partitioning and generalized linear and non-linear models for preference rankings.

Simultaneous threshold interaction modeling approach for paired comparisons rankings

D'Ambrosio, A.
;
Baldassarre, A.;Conversano, C.
2019-01-01

Abstract

Rankings and paired comparisons rankings are ubiquitous in data analysis. Recently, in the literature there has been a growing interest in modeling rank data, especially in trying to explain and/or predict preferences explicitly stated from a sample of judges starting from a set of covariates over a set of alternatives. Both parametric and non-parametric tools have been introduced in order to deal with preference rankings or paired comparison rankings as response variable. In this work we introduce a model dealing with the identification of threshold interaction effects in paired comparisons rankings response data, which integrates recursive partitioning and generalized linear and non-linear models for preference rankings.
2019
9786185180324
STIMA; paired comparisons; preference rankings; trunk modeling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/284234
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