IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i12p3404-3410.html
   My bibliography  Save this article

A fast algorithm for computing least-squares cross-validations for nonparametric conditional kernel density functions

Author

Listed:
  • Ichimura, Tsuyoshi
  • Fukuda, Daisuke

Abstract

Nonparametric conditional density functions are widely used in applied econometric and statistical modelling because they provide enriched information summaries of the relationships between dependent and independent variables. Although least-squares cross-validation is considered to be the best criterion for bandwidth selection of the kernel estimator of the conditional density, the number of computations required for this procedure grows exponentially as the number of observations increases. A fast algorithm is proposed to reduce this computational cost, and its accuracy and efficiency are verified via numerical experiments. A practical application is also presented to demonstrate the algorithm's potential usefulness.

Suggested Citation

  • Ichimura, Tsuyoshi & Fukuda, Daisuke, 2010. "A fast algorithm for computing least-squares cross-validations for nonparametric conditional kernel density functions," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3404-3410, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3404-3410
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00316-8
    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. Jeffrey Racine, 2008. "Nonparametric econometrics: a primer (in Russian)," Quantile, Quantile, issue 4, pages 7-56, March.
    2. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    3. Racine, Jeff, 2002. "Parallel distributed kernel estimation," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 293-302, August.
    4. Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
    5. Jianqing Fan & Tsz Ho Yim, 2004. "A crossvalidation method for estimating conditional densities," Biometrika, Biometrika Trust, vol. 91(4), pages 819-834, December.
    6. Bashtannyk, David M. & Hyndman, Rob J., 2001. "Bandwidth selection for kernel conditional density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 279-298, May.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    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. Fosgerau, Mogens & Fukuda, Daisuke, 2010. "Valuing travel time variability: Characteristics of the travel time distribution on an urban road," MPRA Paper 24330, University Library of Munich, Germany.

    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. George Halkos & Nickolaos Tzeremes, 2012. "Measuring German regions’ environmental efficiency: a directional distance function approach," Letters in Spatial and Resource Sciences, Springer, vol. 5(1), pages 7-16, March.
    2. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "Economic growth and environmental efficiency: Evidence from US regions," Economics Letters, Elsevier, vol. 120(1), pages 48-52.
    3. Halkos, George E. & Tzeremes, Nickolaos G., 2014. "Public sector transparency and countries’ environmental performance: A nonparametric analysis," Resource and Energy Economics, Elsevier, vol. 38(C), pages 19-37.
    4. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 182-189.
    5. Mengistu Assefa Wendimu & Arne Henningsen & Tomasz Gerard Czekaj, 2017. "Incentives and moral hazard: plot level productivity of factory-operated and outgrower-operated sugarcane production in Ethiopia," Agricultural Economics, International Association of Agricultural Economists, vol. 48(5), pages 549-560, September.
    6. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "Carbon dioxide emissions and governance: A nonparametric analysis for the G-20," Energy Economics, Elsevier, vol. 40(C), pages 110-118.
    7. Efromovich, Sam, 2011. "Nonparametric estimation of the anisotropic probability density of mixed variables," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 468-481, March.
    8. Halkos, George & Tzeremes, Nickolaos, 2011. "Regional environmental efficiency and economic growth: NUTS2 evidence from Germany, France and the UK," MPRA Paper 33698, University Library of Munich, Germany.
    9. Tzeremes, Nickolaos G., 2015. "Efficiency dynamics in Indian banking: A conditional directional distance approach," European Journal of Operational Research, Elsevier, vol. 240(3), pages 807-818.
    10. Halkos, George E. & Tzeremes, Nickolaos G., 2012. "Analyzing the Greek renewable energy sector: A Data Envelopment Analysis approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2884-2893.
    11. George Halkos & Roman Matousek & Nickolaos Tzeremes, 2016. "Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 47-77, January.
    12. Chu, Chi-Yang & Henderson, Daniel J. & Parmeter, Christopher F., 2017. "On discrete Epanechnikov kernel functions," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 79-105.
    13. Minviel, Jean Joseph & De Witte, Kristof, 2017. "The influence of public subsidies on farm technical efficiency: A robust conditional nonparametric approach," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1112-1120.
    14. Halkos, George E. & Tzeremes, Nickolaos G., 2014. "The effect of electricity consumption from renewable sources on countries׳ economic growth levels: Evidence from advanced, emerging and developing economies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 166-173.
    15. Subal Kumbhakar & Christopher Parmeter, 2015. "Introduction," Empirical Economics, Springer, vol. 48(1), pages 1-8, February.
    16. Halkos, George & Sundström, Aksel & Tzeremes, Nickolaos, 2013. "Environmental performance and quality of governance: A non-parametric analysis of the NUTS 1-regions in France, Germany and the UK," MPRA Paper 48890, University Library of Munich, Germany.
    17. Tomasz Czekaj & Arne Henningsen, 2013. "Panel Data Specifications in Nonparametric Kernel Regression: An Application to Production Functions," IFRO Working Paper 2013/5, University of Copenhagen, Department of Food and Resource Economics.
    18. McLeod, Logan, 2011. "A nonparametric vs. latent class model of general practitioner utilization: Evidence from Canada," Journal of Health Economics, Elsevier, vol. 30(6), pages 1261-1279.
    19. Panayiotis Tzeremes, 2020. "Productivity, efficiency and firm’s market value: Microeconomic evidence from multinational corporations," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 95-105.

    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:eee:csdana:v:54:y:2010:i:12:p:3404-3410. 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/locate/csda .

    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.