IDEAS home Printed from https://ideas.repec.org/p/wop/humbse/9307.html
   My bibliography  Save this paper

Bandwidth selection in kernel density estimation: a rewiew

Author

Listed:
  • Berwin A. TURLACH

Abstract

No abstract is available for this item.

Suggested Citation

  • Berwin A. TURLACH, "undated". "Bandwidth selection in kernel density estimation: a rewiew," Statistic und Oekonometrie 9307, Humboldt Universitaet Berlin.
    • Handle: RePEc:wop:humbse:9307
    as

    Download full text from publisher

    File URL: ftp://amadeus.wiwi.hu-berlin.de/pub/papers/ise/dp9307.ps.Z
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mammen, Enno, 1990. "A short note on optimal bandwidth selection for kernel estimators," Statistics & Probability Letters, Elsevier, vol. 9(1), pages 23-25, January.
    2. B. W. Silverman, 1982. "Kernel Density Estimation Using the Fast Fourier Transform," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(1), pages 93-99, March.
    3. PARK, Byeong U. & TURLACH, Berwin A., 1992. "Practical performance of several data driven bandwidth selectors," LIDAM Reprints CORE 1001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Marron, J. S., 1989. "Comments on a data based bandwidth selector," Computational Statistics & Data Analysis, Elsevier, vol. 8(2), pages 155-170, July.
    5. Jones, M. C., 1991. "The roles of ISE and MISE in density estimation," Statistics & Probability Letters, Elsevier, vol. 12(1), pages 51-56, July.
    6. Kooperberg, Charles & Stone, Charles J., 1991. "A study of logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 327-347, November.
    7. PARK, Byeong & TURLACH, Berwin, 1992. "Practical performance of several data driven bandwidth selectors," LIDAM Discussion Papers CORE 1992005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Hall, Peter & Wand, Matthew P., 1988. "On the minimization of absolute distance in kernel density estimation," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 311-314, April.
    9. M. C. Jones & H. W. Lotwick, 1984. "A Remark on Algorithm as 176. Kernel Density Estimation Using the Fast Fourier Transform," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 120-122, March.
    10. Park, B. & Kim, W. & Marron, J., 1991. "Asymptotically Best Bandwidth Selectors in Kernel Density Estimation," LIDAM Discussion Papers CORE 1991054, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Marron, J S, 1988. "Automatic Smoothing Parameter Selection: A Survey," Empirical Economics, Springer, vol. 13(3/4), pages 187-208.
    12. Hall, Peter & Marron, J. S., 1987. "Estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 6(2), pages 109-115, November.
    13. HARDLE, Wolfgang & SCOTT, David, 1990. "Smoothing by weighted averaging of rounded points," LIDAM Discussion Papers CORE 1990040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. N/A, 1989. "Comments," ILR Review, Cornell University, ILR School, vol. 43(1), pages 89-102, October.
    15. Hall, Peter. & Wand, M.P., "undated". "On the Accuracy of Binned Kernel Density Estimators," Statistics Working Paper 93003, Australian Graduate School of Management.
    16. Hardle, W. & Marron, J.S. & Wand, Mp., 1990. "Bandwith choice for density derivatives," LIDAM Reprints CORE 945, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Jones, M. C. & Sheather, S. J., 1991. "Using non-stochastic terms to advantage in kernel-based estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 11(6), pages 511-514, June.
    18. Berwin A. TURLACH:, "undated". "Discretization methods for average derivative estimation," Statistic und Oekonometrie 9306, Humboldt Universitaet Berlin.
    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. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Subbiah, Mohan & Fabozzi, Frank J., 2016. "Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 189-201.
    3. Mihaly Ormos & David Zibriczky, 2015. "Entropy-Based Financial Asset Pricing," Papers 1501.01155, arXiv.org.
    4. Wheatley, Spencer & Filimonov, Vladimir & Sornette, Didier, 2016. "The Hawkes process with renewal immigration & its estimation with an EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 120-135.
    5. Vesa Hasu & Kalle Halmevaara & Heikki Koivo, 2011. "An approximation procedure of quantiles using an estimation of kernel method for quality control," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 399-413.
    6. Wang, Qing & Lindsay, Bruce G., 2015. "Improving cross-validated bandwidth selection using subsampling-extrapolation techniques," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 51-71.
    7. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    8. Ossandon Busch, Matias & Sánchez-Martínez, José Manuel & Rodríguez-Martínez, Anahí & Montañez-Enríquez, Ricardo & Martínez-Jaramillo, Serafín, 2022. "Growth at risk: Methodology and applications in an open-source platform," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    9. Lin, Yan-Hui & Jiao, Xin-Lei, 2021. "Adaptive Kernel Auxiliary Particle Filter Method for Degradation State Estimation," Reliability Engineering and System Safety, Elsevier, vol. 211(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. Duc Devroye & J. Beirlant & R. Cao & R. Fraiman & P. Hall & M. Jones & Gábor Lugosi & E. Mammen & J. Marron & C. Sánchez-Sellero & J. Uña & F. Udina & L. Devroye, 1997. "Universal smoothing factor selection in density estimation: theory and practice," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 6(2), pages 223-320, December.
    2. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
    3. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    4. M. M. Salinas-Jimenez, 2003. "Technological change, efficiency gains and capital accumulation in labour productivity growth and convergence: an application to the Spanish regions," Applied Economics, Taylor & Francis Journals, vol. 35(17), pages 1839-1851.
    5. Farmen, Mark & Marron, J. S., 1999. "An assessment of finite sample performance of adaptive methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 143-168, April.
    6. Miguel Reyes & Mario Francisco-Fernández & Ricardo Cao, 2017. "Bandwidth selection in kernel density estimation for interval-grouped data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 527-545, September.
    7. Adriano Z. Zambom & Ronaldo Dias, 2013. "A Review of Kernel Density Estimation with Applications to Econometrics," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 20-42, April.
    8. Gonzalez-Manteiga, W. & Sanchez-Sellero, C. & Wand, M. P., 1996. "Accuracy of binned kernel functional approximations," Computational Statistics & Data Analysis, Elsevier, vol. 22(1), pages 1-16, June.
    9. J. S. Marron & S. S. Chung, 2001. "Presentation of smoothers: the family approach," Computational Statistics, Springer, vol. 16(1), pages 195-207, March.
    10. Teruko Takada, 2001. "Nonparametric density estimation: A comparative study," Economics Bulletin, AccessEcon, vol. 3(16), pages 1-10.
    11. Wolfgang HAERDLE & Marlene MUELLER, "undated". "Nichtparametrische Glaettungsmethoden in der alltaeglichen statistischen Praxis," Statistic und Oekonometrie 9208, Humboldt Universitaet Berlin.
    12. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    13. José E. Chacón & Carlos Tenreiro, 2012. "Exact and Asymptotically Optimal Bandwidths for Kernel Estimation of Density Functionals," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 523-548, September.
    14. Mokkadem, Abdelkader & Pelletier, Mariane, 2020. "Online estimation of integrated squared density derivatives," Statistics & Probability Letters, Elsevier, vol. 166(C).
    15. Hall, Peter & Wolff, Rodney C. L., 1995. "Estimators of integrals of powers of density derivatives," Statistics & Probability Letters, Elsevier, vol. 24(2), pages 105-110, August.
    16. Goutis, Constantinos, 1996. "Nonparametric estimation of a mixing density via the kernel method," DES - Working Papers. Statistics and Econometrics. WS 10437, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Rudolf Grübel, 1994. "Estimation of density functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(1), pages 67-75, March.
    18. Robert J. R. Elliott & Liza Jabbour & Liyun Zhang, 2016. "Firm productivity and importing: Evidence from Chinese manufacturing firms," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(3), pages 1086-1124, August.
    19. Marco BIANCHI, "undated". "A simple and fast method of regime shifts detection based on kernel density estimation," Statistic und Oekonometrie 9316, Humboldt Universitaet Berlin.
    20. Hardle, W. & Marron, J. S., 1995. "Fast and simple scatterplot smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 20(1), pages 1-17, July.

    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:wop:humbse:9307. 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: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/ishubde.html .

    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.