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Regularization techniques in joinpoint regression

Author

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  • Matúš Maciak

    (Charles University in Prague)

  • Ivan Mizera

    (University of Alberta)

Abstract

Joinpoint regression models are popular in various situations (modeling different trends in economics, mortality and incidence series or epidemiology studies and clinical trials). The literature on joinpoint regression mostly focuses on either the frequentist point of view, or discusses Bayesian approaches instead. A model selection step in all these scenarios considers only some limited set of alternatives, from which the final model is chosen. We present a different model estimation approach: the final model is selected out of all possible alternatives admitted by the data. We apply the $$L_{1}$$ L 1 -regularization idea and via the sparsity principle we identify significant joinpoint locations to construct the final model. Some theoretical results and practical examples are given as well.

Suggested Citation

  • Matúš Maciak & Ivan Mizera, 2016. "Regularization techniques in joinpoint regression," Statistical Papers, Springer, vol. 57(4), pages 939-955, December.
    • Handle: RePEc:spr:stpapr:v:57:y:2016:i:4:d:10.1007_s00362-016-0823-2
    DOI: 10.1007/s00362-016-0823-2
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    References listed on IDEAS

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    6. Yu, Binbing & Barrett, Michael J. & Kim, Hyune-Ju & Feuer, Eric J., 2007. "Estimating joinpoints in continuous time scale for multiple change-point models," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2420-2427, February.
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    Cited by:

    1. Maciak, Matúš, 2021. "Quantile LASSO with changepoints in panel data models applied to option pricing," Econometrics and Statistics, Elsevier, vol. 20(C), pages 166-175.
    2. Maciak, Matúš, 2021. "Quantile LASSO in arbitrage-free option markets," Econometrics and Statistics, Elsevier, vol. 18(C), pages 106-116.

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