IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v64y2010i1p45-70.html
   My bibliography  Save this article

Estimation of a k‐monotone density: characterizations, consistency and minimax lower bounds

Author

Listed:
  • Fadoua Balabdaoui
  • Jon A. Wellner

Abstract

The classes of monotone or convex (and necessarily monotone) densities on can be viewed as special cases of the classes of k‐monotone densities on . These classes bridge the gap between the classes of monotone (1‐monotone) and convex decreasing (2‐monotone) densities for which asymptotic results are known, and the class of completely monotone (∞‐monotone) densities on . In this paper we consider non‐parametric maximum likelihood and least squares estimators of a k‐monotone density g0. We prove existence of the estimators and give characterizations. We also establish consistency properties, and show that the estimators are splines of degree k−1 with simple knots. We further provide asymptotic minimax risk lower bounds for estimating the derivatives , at a fixed point x0 under the assumption that .

Suggested Citation

  • Fadoua Balabdaoui & Jon A. Wellner, 2010. "Estimation of a k‐monotone density: characterizations, consistency and minimax lower bounds," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(1), pages 45-70, February.
    • Handle: RePEc:bla:stanee:v:64:y:2010:i:1:p:45-70
    DOI: 10.1111/j.1467-9574.2009.00438.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9574.2009.00438.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9574.2009.00438.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dragi Anevski, 2003. "Estimating the Derivative of a Convex Density," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(2), pages 245-257, May.
    2. Piet Groeneboom & Geurt Jongbloed & Jon A. Wellner, 2008. "The Support Reduction Algorithm for Computing Non‐Parametric Function Estimates in Mixture Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 385-399, September.
    3. Jongbloed, Geurt, 2000. "Minimax lower bounds and moduli of continuity," Statistics & Probability Letters, Elsevier, vol. 50(3), pages 279-284, November.
    4. Gneiting, Tilmann, 1999. "Radial Positive Definite Functions Generated by Euclid's Hat," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 88-119, April.
    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. Giguelay, J. & Huet, S., 2018. "Testing k-monotonicity of a discrete distribution. Application to the estimation of the number of classes in a population," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 96-115.
    2. Chee, Chew-Seng & Wang, Yong, 2016. "Nonparametric estimation of species richness using discrete k-monotone distributions," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 107-118.
    3. Fadoua Balabdaoui, 2014. "Global convergence of the log-concave MLE when the true distribution is geometric," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 21-59, March.
    4. Chee, Chew-Seng & Wang, Yong, 2014. "Least squares estimation of a k-monotone density function," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 209-216.
    5. Chee, Chew-Seng & Wang, Yong, 2013. "Minimum quadratic distance density estimation using nonparametric mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 1-16.
    6. Balabdaoui, Fadoua & Kulagina, Yulia, 2020. "Completely monotone distributions: Mixing, approximation and estimation of number of species," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

    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. Pavlides, Marios G. & Wellner, Jon A., 2012. "Nonparametric estimation of multivariate scale mixtures of uniform densities," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 71-89.
    2. Fils-Villetard, A. & Guillou, A. & Segers, J., 2005. "Projection Estimates of Constrained Functional Parameters," Discussion Paper 2005-111, Tilburg University, Center for Economic Research.
    3. Madeleine Cule & Richard Samworth & Michael Stewart, 2010. "Maximum likelihood estimation of a multi‐dimensional log‐concave density," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 545-607, November.
    4. repec:jss:jstsof:36:i02 is not listed on IDEAS
    5. Gneiting, Tilmann, 2002. "Compactly Supported Correlation Functions," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 493-508, November.
    6. Feng, Oliver Y. & Chen, Yining & Han, Qiyang & Carroll, Raymond J & Samworth, Richard J., 2022. "Nonparametric, tuning-free estimation of S-shaped functions," LSE Research Online Documents on Economics 111889, London School of Economics and Political Science, LSE Library.
    7. Balabdaoui, Fadoua & Kulagina, Yulia, 2020. "Completely monotone distributions: Mixing, approximation and estimation of number of species," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    8. Andrea Barth & Fred Espen Benth & Jurgen Potthoff, 2011. "Hedging of Spatial Temperature Risk with Market-Traded Futures," Applied Mathematical Finance, Taylor & Francis Journals, vol. 18(2), pages 93-117.
    9. Fils-Villetard, A. & Guillou, A. & Segers, J., 2005. "Projection Estimates of Constrained Functional Parameters," Other publications TiSEM fe25c070-c313-4369-a6a5-8, Tilburg University, School of Economics and Management.
    10. Castañer, A. & Claramunt, M.M. & Lefèvre, C. & Loisel, S., 2015. "Discrete Schur-constant models," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 343-362.
    11. Azadbakhsh, Mahdis & Jankowski, Hanna & Gao, Xin, 2014. "Computing confidence intervals for log-concave densities," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 248-264.
    12. Chee, Chew-Seng, 2017. "A mixture model-based nonparametric approach to estimating a count distribution," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 34-44.
    13. Sajti, Szilárd, 2023. "Domain-domain correlation functions used in off-specular scattering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    14. Balabdaoui, Fadoua & Rufibach, Kaspar, 2008. "A second Marshall inequality in convex estimation," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 118-126, February.
    15. Rufibach, Kaspar, 2010. "An active set algorithm to estimate parameters in generalized linear models with ordered predictors," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1442-1456, June.
    16. Alonso-Malaver, C.E. & Porcu, E. & Giraldo, R., 2015. "Multivariate and multiradial Schoenberg measures with their dimension walks," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 251-265.
    17. Dümbgen, Lutz & Mösching, Alexandre & Strähl, Christof, 2021. "Active set algorithms for estimating shape-constrained density ratios," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    18. Furrer, Reinhard & Bengtsson, Thomas, 2007. "Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants," Journal of Multivariate Analysis, Elsevier, vol. 98(2), pages 227-255, February.
    19. Yining Chen & Richard J. Samworth, 2016. "Generalized additive and index models with shape constraints," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 729-754, September.
    20. Moreva, Olga & Schlather, Martin, 2023. "Bivariate covariance functions of Pólya type," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    21. Durot, Cécile & Huet, Sylvie & Koladjo, François & Robin, Stéphane, 2013. "Least-squares estimation of a convex discrete distribution," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 282-298.

    More about this item

    Statistics

    Access and download statistics

    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:bla:stanee:v:64:y:2010:i:1:p:45-70. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.