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A filtered polynomial approach to density estimation

Author

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  • Dominik Heinzmann

Abstract

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Suggested Citation

  • Dominik Heinzmann, 2008. "A filtered polynomial approach to density estimation," Computational Statistics, Springer, vol. 23(3), pages 343-360, July.
    • Handle: RePEc:spr:compst:v:23:y:2008:i:3:p:343-360
    DOI: 10.1007/s00180-007-0070-z
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    Cited by:

    1. Silvia Montagna & Vanessa Orani & Raffaele Argiento, 2021. "Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 573-604, June.
    2. Kevin Murray & Samuel Müller & Berwin Turlach, 2013. "Revisiting fitting monotone polynomials to data," Computational Statistics, Springer, vol. 28(5), pages 1989-2005, October.
    3. Jochen Ranger & Jorg-Tobias Kuhn, 2012. "A flexible latent trait model for response times in tests," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 31-47, January.

    More about this item

    Keywords

    Density estimation; Empirical transformation; Filtered polynomial; MCMC simulation; Multivariate settings; C63;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    Statistics

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