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Smoothing in Ordinal Regression: An Application to Sensory Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ejike R. Ugba & Daniel Mörlein & Jan Gertheiss, 2021. "Smoothing in Ordinal Regression: An Application to Sensory Data," Stats, MDPI, vol. 4(3), pages 1-18, July.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:3:p:37-633:d:598374
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    References listed on IDEAS

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