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An Improved Transformation-Based Kernel Estimator of Densities on the Unit Interval

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  • Kuangyu Wen
  • Ximing Wu

Abstract

The kernel density estimator (KDE) suffers boundary biases when applied to densities on bounded supports, which are assumed to be the unit interval. Transformations mapping the unit interval to the real line can be used to remove boundary biases. However, this approach may induce erratic tail behaviors when the estimated density of transformed data is transformed back to its original scale. We propose a modified, transformation-based KDE that employs a tapered and tilted back-transformation. We derive the theoretical properties of the new estimator and show that it asymptotically dominates the naive transformation based estimator while maintains its simplicity. We then propose three automatic methods of smoothing parameter selection. Our Monte Carlo simulations demonstrate the good finite sample performance of the proposed estimator, especially for densities with poles near the boundaries. An example with real data is provided.

Suggested Citation

  • Kuangyu Wen & Ximing Wu, 2015. "An Improved Transformation-Based Kernel Estimator of Densities on the Unit Interval," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 773-783, June.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:510:p:773-783
    DOI: 10.1080/01621459.2014.969426
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    Cited by:

    1. Kairat Mynbaev & Carlos Martins-Filho, 2019. "Unified estimation of densities on bounded and unbounded domains," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 853-887, August.
    2. Tepegjozova Marija & Zhou Jing & Claeskens Gerda & Czado Claudia, 2022. "Nonparametric C- and D-vine-based quantile regression," Dependence Modeling, De Gruyter, vol. 10(1), pages 1-21, January.

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