Simultaneous threshold interaction modeling approach for paired comparisons rankings
Antonio D’Ambrosio, Antonio Baldassarre, Claudio Conversano · UNICA IRIS Institutional Research Information System (University of Cagliari) · 2019
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.