IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v64y2003i2p169-179.html
   My bibliography  Save this article

Local linear regression estimation of the variogram

Author

Listed:
  • García-Soidán, Pilar H.
  • González-Manteiga, Wenceslao
  • Febrero-Bande, Manuel

Abstract

In this work, we introduce the local linear semivariogram. Several properties of this estimator are established and compared with those of the Nadaraya-Watson semivariogram. Finally, an adaptation of Shapiro and Botha's fit is applied to produce a valid estimator.

Suggested Citation

  • García-Soidán, Pilar H. & González-Manteiga, Wenceslao & Febrero-Bande, Manuel, 2003. "Local linear regression estimation of the variogram," Statistics & Probability Letters, Elsevier, vol. 64(2), pages 169-179, August.
    • Handle: RePEc:eee:stapro:v:64:y:2003:i:2:p:169-179
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(03)00149-4
    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. Shapiro, A. & Botha, J. D., 1991. "Variogram fitting with a general class of conditionally nonnegative definite functions," Computational Statistics & Data Analysis, Elsevier, vol. 11(1), pages 87-96, January.
    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. Bowman, Adrian W. & Crujeiras, Rosa M., 2013. "Inference for variograms," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 19-31.
    2. Castillo-Páez, Sergio & Fernández-Casal, Rubén & García-Soidán, Pilar, 2019. "A nonparametric bootstrap method for spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 1-15.

    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. Rolando Biscay Lirio & Dunia Camejo & Jean-Michel Loubes & Lilian Muñiz Alvarez, 2014. "Estimation of covariance functions by a fully data-driven model selection procedure and its application to Kriging spatial interpolation of real rainfall data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 149-174, June.
    2. A. Meilán-Vila & R. Fernández-Casal & R. M. Crujeiras & M. Francisco-Fernández, 2021. "A computational validation for nonparametric assessment of spatial trends," Computational Statistics, Springer, vol. 36(4), pages 2939-2965, December.
    3. Genton, Marc G. & Gorsich, David J., 2002. "Nonparametric variogram and covariogram estimation with Fourier-Bessel matrices," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 47-57, November.
    4. Dursun Aydin & Ersin Yilmaz, 2021. "Censored Nonparametric Time-Series Analysis with Autoregressive Error Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 169-202, August.
    5. Carmack, Patrick S. & Spence, Jeffrey S. & Schucany, William R. & Gunst, Richard F. & Lin, Qihua & Haley, Robert W., 2012. "A new class of semiparametric semivariogram and nugget estimators," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1737-1747.
    6. Sompop Moonchai & Nawinda Chutsagulprom, 2020. "Semiparametric Semivariogram Modeling with a Scaling Criterion for Node Spacing: A Case Study of Solar Radiation Distribution in Thailand," Mathematics, MDPI, vol. 8(12), pages 1-16, December.
    7. Bowman, Adrian W. & Crujeiras, Rosa M., 2013. "Inference for variograms," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 19-31.
    8. Rehman, Salim U. & Shapiro, A., 1996. "An integral transform approach to cross-variograms modeling," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 213-233, July.
    9. Castillo-Páez, Sergio & Fernández-Casal, Rubén & García-Soidán, Pilar, 2019. "A nonparametric bootstrap method for spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 1-15.
    10. Ghulam A. Qadir & Ying Sun, 2021. "Semiparametric estimation of cross‐covariance functions for multivariate random fields," Biometrics, The International Biometric Society, vol. 77(2), pages 547-560, June.
    11. Miro R. Powojowski, 2008. "Isotropic spectral additive models of the covariogram," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 739-753, September.

    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:stapro:v:64:y:2003:i:2:p:169-179. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.