IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v112y2017i520p1587-1597.html
   My bibliography  Save this article

Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods

Author

Listed:
  • Kyle R. White
  • Leonard A. Stefanski
  • Yichao Wu

Abstract

This article develops a nonparametric shrinkage and selection estimator via the measurement error selection likelihood approach recently proposed by Stefanski, Wu, and White. The measurement error kernel regression operator (MEKRO) has the same form as the Nadaraya–Watson kernel estimator, but optimizes a measurement error model selection likelihood to estimate the kernel bandwidths. Much like LASSO or COSSO solution paths, MEKRO results in solution paths depending on a tuning parameter that controls shrinkage and selection via a bound on the harmonic mean of the pseudo-measurement error standard deviations. We use small-sample-corrected AIC to select the tuning parameter. Large-sample properties of MEKRO are studied and small-sample properties are explored via Monte Carlo experiments and applications to data. Supplementary materials for this article are available online.

Suggested Citation

  • Kyle R. White & Leonard A. Stefanski & Yichao Wu, 2017. "Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1587-1597, October.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:520:p:1587-1597
    DOI: 10.1080/01621459.2016.1222287
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2016.1222287
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2016.1222287?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
    ---><---

    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. Pradeep Ravikumar & John Lafferty & Han Liu & Larry Wasserman, 2009. "Sparse additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 1009-1030, November.
    3. L. A. Stefanski & Yichao Wu & Kyle White, 2014. "Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 574-589, June.
    4. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
    Full references (including those not matched with items 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. Hans R. A. Koster & Jos N. van Ommeren & Piet Rietveld, 2016. "Historic amenities, income and sorting of households," Journal of Economic Geography, Oxford University Press, vol. 16(1), pages 203-236.
    2. Hang Yu & Yuanjia Wang & Donglin Zeng, 2023. "A general framework of nonparametric feature selection in high‐dimensional data," Biometrics, The International Biometric Society, vol. 79(2), pages 951-963, June.
    3. 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.
    4. Li, Qi & Yang, Jian & Hsiao, Cheng & Chang, Young-Jae, 2005. "The relationship between stock returns and volatility in international stock markets," Journal of Empirical Finance, Elsevier, vol. 12(5), pages 650-665, December.
    5. 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.
    6. Asaftei, Gabriel & Parmeter, Christopher F., 2010. "Market power, EU integration and privatization: The case of Romania," Journal of Comparative Economics, Elsevier, vol. 38(3), pages 340-356, September.
    7. 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.
    8. Fabien Candau & Elisa Dienesch, 2015. "Spatial distribution of skills and regional trade integration," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 451-488, March.
    9. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    10. Pandit, Mahesh & Paudel, Krishna P. & Hinson, Roger A., 2012. "Intensity of Integrated Pest Management (IPM) Practices Adoption by U.S. Nursery Crop Producers," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124942, Agricultural and Applied Economics Association.
    11. Christina Felfe & Martin Huber, 2017. "Does preschool boost the development of minority children?: the case of Roma children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 475-502, February.
    12. 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.
    13. Christopher Parmeter & Kai Sun & Daniel Henderson & Subal Kumbhakar, 2014. "Estimation and inference under economic restrictions," Journal of Productivity Analysis, Springer, vol. 41(1), pages 111-129, February.
    14. W. Walls, 2009. "Screen wars, star wars, and sequels," Empirical Economics, Springer, vol. 37(2), pages 447-461, October.
    15. Henderson, Daniel J. & Millimet, Daniel L., 2005. "Environmental regulation and US state-level production," Economics Letters, Elsevier, vol. 87(1), pages 47-53, April.
    16. Geraldine Henningsen & Arne Henningsen & Christian Henning, 2015. "Transaction costs and social networks in productivity measurement," Empirical Economics, Springer, vol. 48(1), pages 493-515, February.
    17. Daniel J. Henderson, 2009. "A Non‐parametric Examination of Capital–Skill Complementarity," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 519-538, August.
    18. Daniel J. Henderson, 2010. "A test for multimodality of regression derivatives with application to nonparametric growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 458-480.
    19. repec:clg:wpaper:2008-28 is not listed on IDEAS
    20. Mika Kortelainen & Simo Leppänen, 2013. "Public and private capital productivity in Russia: a non-parametric investigation," Empirical Economics, Springer, vol. 45(1), pages 193-216, August.
    21. 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.

    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:taf:jnlasa:v:112:y:2017:i:520:p:1587-1597. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.