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On Reverse Shrinkage Effects and Shrinkage Overshoot

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  • Pascal Jordan

    (University of Hamburg)

Abstract

Given a squared Euclidean norm penalty, we examine some less well-known properties of shrinkage estimates. In particular, we highlight that it is possible for some components of the shrinkage estimator to be placed further away from the prior mean than the original estimate. An analysis of this effect is provided within three different modeling settings—encompassing linear, logistic, and ordinal regression models. Additional simulations show that the outlined effect is not a mathematical artefact, but likely to occur in practice. As a byproduct, they also highlight the possibilities of sign reversals (“overshoots”) for shrinkage estimates. We point out practical consequences and challenges, which might arise from the observed effects with special emphasis on psychometrics.

Suggested Citation

  • Pascal Jordan, 2023. "On Reverse Shrinkage Effects and Shrinkage Overshoot," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 274-301, March.
    • Handle: RePEc:spr:psycho:v:88:y:2023:i:1:d:10.1007_s11336-022-09872-8
    DOI: 10.1007/s11336-022-09872-8
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    References listed on IDEAS

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    1. Shelby Haberman & Sandip Sinharay, 2010. "Reporting of Subscores Using Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 209-227, June.
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    6. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    7. Pascal Jordan & Martin Spiess, 2018. "A New Explanation and Proof of the Paradoxical Scoring Results in Multidimensional Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 831-846, December.
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