Smoothing in Ordinal Regression: An Application to Sensory Data

Ejike R. Ugba, Daniel Mörlein and Jan Gertheiss
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Ejike R. Ugba: Department of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, 22043 Hamburg, Germany
Daniel Mörlein: Department of Animal Sciences, Faculty of Agricultural Sciences, Georg August University, 37077 Göttingen, Germany
Jan Gertheiss: Department of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, 22043 Hamburg, Germany

Stats, 2021, vol. 4, issue 3, 1-18

Abstract: The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects of predictors (androstenone and skatole) vary between response categories. For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories. The usefulness of SERP is further demonstrated through a simulation study. Besides flexible and accurate modeling, SERP also enables fitting of parameters in cases where the pure, unpenalized non-proportional odds model fails to converge.

Keywords: animal welfare; Brant test; categorical data; quality control; regularization; sensometrics (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
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