IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v32y2023i4d10.1007_s10260-023-00687-0.html
   My bibliography  Save this article

Kernel regression for errors-in-variables problems in the circular domain

Author

Listed:
  • Marco Di Marzio

    (DSFPEQ, University of Chieti-Pescara)

  • Stefania Fensore

    (DSFPEQ, University of Chieti-Pescara)

  • Charles C. Taylor

    (University of Leeds)

Abstract

We study the problem of estimating a regression function when the predictor and/or the response are circular random variables in the presence of measurement errors. We propose estimators whose weight functions are deconvolution kernels defined according to the nature of the involved variables. We derive the asymptotic properties of the proposed estimators and consider possible generalizations and extensions. We provide some simulation results and a real data case study to illustrate and compare the proposed methods.

Suggested Citation

  • Marco Di Marzio & Stefania Fensore & Charles C. Taylor, 2023. "Kernel regression for errors-in-variables problems in the circular domain," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1217-1237, October.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:4:d:10.1007_s10260-023-00687-0
    DOI: 10.1007/s10260-023-00687-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-023-00687-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-023-00687-0?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. Macro Di Marzio & Agnese Panzera & Charles C. Taylor, 2012. "Non-parametric smoothing and prediction for nonlinear circular time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(4), pages 620-630, July.
    2. Raymond J. Carroll & Aurore Delaigle & Peter Hall, 2007. "Non‐parametric regression estimation from data contaminated by a mixture of Berkson and classical errors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 859-878, November.
    3. Raymond J. Carroll & Peter Hall, 2004. "Low order approximations in deconvolution and regression with errors in variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 31-46, February.
    4. Delaigle, Aurore & Fan, Jianqing & Carroll, Raymond J., 2009. "A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 348-359.
    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. Delaigle, Aurore & Meister, Alexander, 2007. "Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1416-1426, December.
    2. Yin, Zanhua & Gao, Wei & Tang, Man-Lai & Tian, Guo-Liang, 2013. "Estimation of nonparametric regression models with a mixture of Berkson and classical errors," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1151-1162.
    3. Julie McIntyre & Brent A. Johnson & Stephen M. Rappaport, 2018. "Monte Carlo methods for nonparametric regression with heteroscedastic measurement error," Biometrics, The International Biometric Society, vol. 74(2), pages 498-505, June.
    4. Marcus Groß, 2016. "Modeling body height in prehistory using a spatio-temporal Bayesian errors-in variables model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 289-311, July.
    5. Huijun Guo & Youming Liu, 2019. "Regression estimation under strong mixing data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 553-576, June.
    6. Delaigle, Aurore & Fan, Jianqing & Carroll, Raymond J., 2009. "A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 348-359.
    7. Carrasco, Marine & Florens, Jean-Pierre, 2011. "A Spectral Method For Deconvolving A Density," Econometric Theory, Cambridge University Press, vol. 27(3), pages 546-581, June.
    8. Wu, Ximing & Perloff, Jeffrey M., 2007. "Information-Theoretic Deconvolution Approximation of Treatment Effect Distribution," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9vd036zx, Department of Agricultural & Resource Economics, UC Berkeley.
    9. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.
    10. Jan Beran & Britta Steffens & Sucharita Ghosh, 2022. "On nonparametric regression for bivariate circular long-memory time series," Statistical Papers, Springer, vol. 63(1), pages 29-52, February.
    11. Hu, Yingyao & Schennach, Susanne & Shiu, Ji-Liang, 2022. "Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 226(2), pages 269-294.
    12. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    13. Staudenmayer, John & Ruppert, David & Buonaccorsi, John P., 2008. "Density Estimation in the Presence of Heteroscedastic Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 726-736, June.
    14. Thomas, Laine & Stefanski, Leonard A. & Davidian, Marie, 2013. "Moment adjusted imputation for multivariate measurement error data with applications to logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 15-24.
    15. Guo, Linruo & Song, Weixing & Shi, Jianhong, 2022. "Estimating multivariate density and its derivatives for mixed measurement error data," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    16. Hao Dong & Taisuke Otsu & Luke Taylor, 2022. "Nonparametric estimation of additive models with errors-in-variables," Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1164-1204, November.
    17. A. Delaigle & P. Hall & J. R. Wishart, 2014. "New approaches to nonparametric and semiparametric regression for univariate and multivariate group testing data," Biometrika, Biometrika Trust, vol. 101(3), pages 567-585.
    18. Hao Dong & Yuya Sasaki, 2022. "Estimation of average derivatives of latent regressors: with an application to inference on buffer-stock saving," Departmental Working Papers 2204, Southern Methodist University, Department of Economics.
    19. Carroll, Raymond J. & Delaigle, Aurore & Hall, Peter, 2009. "Nonparametric Prediction in Measurement Error Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 993-1003.
    20. Wu, Ximing & Perloff, Jeffrey M., 2007. "Information-Theoretic Deconvolution Approximation of Treatment Effect Distribution," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9vd036zx, Department of Agricultural & Resource Economics, UC Berkeley.

    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:spr:stmapp:v:32:y:2023:i:4:d:10.1007_s10260-023-00687-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.