IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v86y2005i2p291-296.html
   My bibliography  Save this article

Uniform convergence rate of kernel estimation with mixed categorical and continuous data

Author

Listed:
  • Li, Qi
  • Ouyang, Desheng

Abstract

No abstract is available for this item.

Suggested Citation

  • Li, Qi & Ouyang, Desheng, 2005. "Uniform convergence rate of kernel estimation with mixed categorical and continuous data," Economics Letters, Elsevier, vol. 86(2), pages 291-296, February.
    • Handle: RePEc:eee:ecolet:v:86:y:2005:i:2:p:291-296
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(04)00296-4
    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. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    2. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    3. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
    4. Elias Masry, 1996. "Multivariate Local Polynomial Regression For Time Series:Uniform Strong Consistency And Rates," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(6), pages 571-599, November.
    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. Daniel J. Henderson & Subal C. Kumbhakar, 2006. "Public and Private Capital Productivity Puzzle: A Nonparametric Approach," Southern Economic Journal, John Wiley & Sons, vol. 73(1), pages 219-232, July.
    2. McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    3. Spyros Vliamos & Nickolaos Tzeremes, 2012. "Factors Influencing Entrepreneurial Process and Firm Start-Ups: Evidence from Central Greece," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 3(3), pages 250-264, September.
    4. Daniel J. Henderson & Alexandre Olbrecht & Solomon W. Polachek, 2006. "Do Former College Athletes Earn More at Work?: A Nonparametric Assessment," Journal of Human Resources, University of Wisconsin Press, vol. 41(3).
    5. W. Walls, 2009. "Screen wars, star wars, and sequels," Empirical Economics, Springer, vol. 37(2), pages 447-461, October.
    6. Masayuki Hirukawa & Irina Murtazashvili & Artem Prokhorov, 2022. "Uniform convergence rates for nonparametric estimators smoothed by the beta kernel," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1353-1382, September.
    7. Pedro H. C. Sant'Anna & Qi Xu, 2023. "Difference-in-Differences with Compositional Changes," Papers 2304.13925, arXiv.org.
    8. Li, Zheng & Rejesus, Roderick M. & Zheng, Xiaoyong, 2018. "Nonparametric Estimation and Inference of Production Risk with Categorical Variables," 2018 Annual Meeting, August 5-7, Washington, D.C. 274400, Agricultural and Applied Economics Association.
    9. repec:clg:wpaper:2008-28 is not listed on IDEAS

    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. Daniel J. Henderson & Alexandre Olbrecht & Solomon W. Polachek, 2006. "Do Former College Athletes Earn More at Work?: A Nonparametric Assessment," Journal of Human Resources, University of Wisconsin Press, vol. 41(3).
    2. Nicholas Kiefer & Jeffrey Racine, 2009. "The smooth Colonel meets the Reverend," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(5), pages 521-533.
    3. Spyros Vliamos & Nickolaos Tzeremes, 2012. "Factors Influencing Entrepreneurial Process and Firm Start-Ups: Evidence from Central Greece," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 3(3), pages 250-264, September.
    4. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.
    5. Chi-Yang Chu & Daniel J. Henderson & Christopher F. Parmeter, 2015. "Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data," Econometrics, MDPI, vol. 3(2), pages 1-16, March.
    6. 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.
    7. Simar, Leopold & Zelenyuk, Valentin, 2011. "To Smooth or Not to Smooth? The Case of Discrete Variables in Nonparametric Regressions," LIDAM Discussion Papers ISBA 2011042, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2016. "Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors," Econometrics, MDPI, vol. 4(2), pages 1-27, April.
    9. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    10. Lin, Wei & Cai, Zongwu & Li, Zheng & Su, Li, 2015. "Optimal smoothing in nonparametric conditional quantile derivative function estimation," Journal of Econometrics, Elsevier, vol. 188(2), pages 502-513.
    11. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    12. Qi Li & Juan Lin & Jeffrey S. Racine, 2013. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 57-65, January.
    13. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    14. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    15. Besstremyannaya, Galina, 2015. "Measuring the effect of health insurance companies on the quality of healthcare systems with kernel and parametric regressions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 3-20.
    16. Michael S. Delgado & Daniel J. Henderson & Christopher F. Parmeter, 2014. "Does Education Matter for Economic Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 334-359, June.
    17. Nolwenn Roudaut & Anne Vanhems, 2012. "Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach," Journal of Productivity Analysis, Springer, vol. 37(2), pages 155-169, April.
    18. Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2017. "Are University Admissions Academically Fair?," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 449-464, July.
    19. Daniel J. Henderson & Andrew Houtenville & Le Wang, 2017. "The Distribution of Returns to Education for People with Disabilities," Journal of Labor Research, Springer, vol. 38(3), pages 261-282, September.
    20. Henderson, Daniel J. & Polachek, Solomon W. & Wang, Le, 2011. "Heterogeneity in schooling rates of return," Economics of Education Review, Elsevier, vol. 30(6), pages 1202-1214.

    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:ecolet:v:86:y:2005:i:2:p:291-296. 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/ecolet .

    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.