Modelling Heterogeneity in Paired Comparison Data - an L1 Penalty Approach with an Application to Party Preference Data

Schauberger, Gunther and Tutz, Gerhard (19. August 2015): Modelling Heterogeneity in Paired Comparison Data - an L1 Penalty Approach with an Application to Party Preference Data. Department of Statistics: Technical Reports, No.183

Abstract

In traditional paired comparison models heterogeneity in the population is simply ignored and it is assumed that all persons have the same preference structure. Here, a new method to model heterogeneity in paired comparison data is proposed. The preference of an item over another item is explicitly modelled as depending on measurements on the subjects. Therefore, the model allows for heterogeneity between subjects as the preference for an item can vary across subjects depending on subject-specific covariates. Since by construction the model contains a large number of parameters we propose to use penalized estimation procedures to obtain estimates of the parameters. The used regularized estimation approach penalizes the differences between the parameters corresponding to single covariates. It enforces variable selection and allows to find clusters of items with respect to covariates. We consider simple binary but also ordinal paired comparisons models. The method is applied to data from a pre-election study from Germany.

• Item Type: Paper
• Form of publication: Preprint
• Keywords: BTL-Lasso; paired comparison; Bradley-Terry-Luce model; Lasso; heterogeneity
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Subjects: 500 Science > 510 Mathematics
• Language: English