IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v46y2000i2p133-147.html
   My bibliography  Save this article

Logspline density estimation for binned data

Author

Listed:
  • Koo, Ja-Yong
  • Kooperberg, Charles

Abstract

In this paper we consider logspline density estimation for binned data. Rates of convergence are established when the log-density function is assumed to be in a Besov space. An algorithm involving a procedure similar to maximum likelihood, stepwise knot addition, and stepwise knot deletion is proposed for the estimation of the density function based upon binned data. Numerical examples are used to show the finite-sample performance of inference based on the logspline density estimation.

Suggested Citation

  • Koo, Ja-Yong & Kooperberg, Charles, 2000. "Logspline density estimation for binned data," Statistics & Probability Letters, Elsevier, vol. 46(2), pages 133-147, January.
  • Handle: RePEc:eee:stapro:v:46:y:2000:i:2:p:133-147
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(99)00097-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kooperberg, Charles & Stone, Charles J., 1991. "A study of logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 327-347, November.
    2. Koo, Ja-Yong, 1996. "Bivariate B-splines for tensor logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 31-42, January.
    3. Koo, Ja-Yong & Kim, Woo-Chul, 1996. "Wavelet density estimation by approximation of log-densities," Statistics & Probability Letters, Elsevier, vol. 26(3), pages 271-278, February.
    4. Hall, Peter & Wand, M. P., 1996. "On the Accuracy of Binned Kernel Density Estimators," Journal of Multivariate Analysis, Elsevier, vol. 56(2), pages 165-184, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bak, Kwan-Young & Jhong, Jae-Hwan & Lee, JungJun & Shin, Jae-Kyung & Koo, Ja-Yong, 2021. "Penalized logspline density estimation using total variation penalty," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    2. Wang, B. & Wertelecki, W., 2013. "Density estimation for data with rounding errors," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 4-12.
    3. Federico Palacios-González & Rosa M. García-Fernández, 2020. "A faster algorithm to estimate multiresolution densities," Computational Statistics, Springer, vol. 35(3), pages 1207-1230, September.
    4. Lambert, Philippe, 2011. "Smooth semiparametric and nonparametric Bayesian estimation of bivariate densities from bivariate histogram data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 429-445, January.
    5. Lopes, Hedibert F. & Dias, Ronaldo, 2011. "Bayesian mixture of parametric and nonparametric density estimation: A Misspecification Problem," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
    6. Papkov, Galen I. & Scott, David W., 2010. "Local-moment nonparametric density estimation of pre-binned data," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3421-3429, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koo, Ja-Yong, 1998. "Convergence Rates for Logspline Tomography," Journal of Multivariate Analysis, Elsevier, vol. 67(2), pages 367-384, November.
    2. Tomas Ruzgas & Mantas Lukauskas & Gedmantas Čepkauskas, 2021. "Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    3. Kirkby, J. Lars & Leitao, Álvaro & Nguyen, Duy, 2021. "Nonparametric density estimation and bandwidth selection with B-spline bases: A novel Galerkin method," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    4. Bak, Kwan-Young & Jhong, Jae-Hwan & Lee, JungJun & Shin, Jae-Kyung & Koo, Ja-Yong, 2021. "Penalized logspline density estimation using total variation penalty," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    5. Michel Harel & Jean-François Lenain & Joseph Ngatchou-Wandji, 2016. "Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 296-321, June.
    6. Holmström, Lasse, 2000. "The Accuracy and the Computational Complexity of a Multivariate Binned Kernel Density Estimator," Journal of Multivariate Analysis, Elsevier, vol. 72(2), pages 264-309, February.
    7. Chang, Meng-Shiuh & Wu, Ximing, 2015. "Transformation-based nonparametric estimation of multivariate densities," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 71-88.
    8. Jérémie Bigot & Sébastien Van Bellegem, 2009. "Log‐density Deconvolution by Wavelet Thresholding," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 749-763, December.
    9. Göran Kauermann & Christian Schellhase & David Ruppert, 2013. "Flexible Copula Density Estimation with Penalized Hierarchical B-splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 685-705, December.
    10. Hadrich, Atizez & Zribi, Mourad & Masmoudi, Afif, 2016. "Bayesian expectation maximization algorithm by using B-splines functions: Application in image segmentation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 120(C), pages 50-63.
    11. Ronaldo Dias & Nancy L. Garcia & Guilherme Ludwig & Marley A. Saraiva, 2015. "Aggregated functional data model for near-infrared spectroscopy calibration and prediction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(1), pages 127-143, January.
    12. Meintanis, S. & Ushakov, N. G., 2004. "Binned goodness-of-fit tests based on the empirical characteristic function," Statistics & Probability Letters, Elsevier, vol. 69(3), pages 305-314, September.
    13. Slone, D.H., 2011. "Increasing accuracy of dispersal kernels in grid-based population models," Ecological Modelling, Elsevier, vol. 222(3), pages 573-579.
    14. Cribari-Neto, Francisco, 1993. "The Cyclical Component in Brazilian GDP," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 13(1), April.
    15. Sain, Stephan R., 2002. "Multivariate locally adaptive density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 165-186, April.
    16. Lopes, Hedibert F. & Dias, Ronaldo, 2011. "Bayesian mixture of parametric and nonparametric density estimation: A Misspecification Problem," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
    17. Liu, Yang & Ruppert, David, 2021. "Density estimation on a network," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    18. J. S. Marron & S. S. Chung, 2001. "Presentation of smoothers: the family approach," Computational Statistics, Springer, vol. 16(1), pages 195-207, March.
    19. Song, Seongjoo, 2010. "Lévy density estimation via information projection onto wavelet subspaces," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1623-1632, November.
    20. Vincent J. Carey & Carol J. Baker & Richard Platt, 2001. "Bayesian Inference on Protective Antibody Levels Using Case‐Control Data," Biometrics, The International Biometric Society, vol. 57(1), pages 135-142, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:46:y:2000:i:2:p:133-147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.