IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i11p2962-2974.html
   My bibliography  Save this article

Kernel interpolation

Author

Listed:
  • Mühlenstädt, Thomas
  • Kuhnt, Sonja

Abstract

Surrogate interpolation models for time-consuming computer experiments are being increasingly used in scientific and engineering problems. A new interpolation method, based on Delaunay triangulations and related to inverse distance weighting, is introduced. This method not only provides an interpolator but also uncertainty bands to judge the local fit, in contrast to methods such as radial basis functions. Compared to the classical Kriging approach, it shows a better performance in specific cases of small data sets and data with non-stationary behavior.

Suggested Citation

  • Mühlenstädt, Thomas & Kuhnt, Sonja, 2011. "Kernel interpolation," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2962-2974, November.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:11:p:2962-2974
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311001678
    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. Hartman, Linda & Hossjer, Ola, 2008. "Fast kriging of large data sets with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2331-2349, January.
    2. Host, Gudmund, 1999. "Kriging by local polynomials," Computational Statistics & Data Analysis, Elsevier, vol. 29(3), pages 295-312, January.
    3. Notz W.I., 2003. "Topics in Optimal Design," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 249-250, January.
    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. Montero, José-María & Fernández-Avilés, Gema, 2015. "Functional Kriging Prediction of Pollution Series: The Geostatistical Alternative for Spatially-fixed Data/Predicción de series de contaminación mediante kriging funcional. La alternativa geoestadísti," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 33, pages 145-179, Enero.
    2. Furrer, Reinhard & Bachoc, François & Du, Juan, 2016. "Asymptotic properties of multivariate tapering for estimation and prediction," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 177-191.
    3. Jason S. Byers & Jeff Gill, 2022. "Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions," Mathematics, MDPI, vol. 10(21), pages 1-23, November.
    4. Zhao, Shan & Wei, G. W., 2003. "Jump process for the trend estimation of time series," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 219-241, February.
    5. Stephen A. Samaha & Wagner A. Kamakura, 2008. "Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(4), pages 717-751, December.
    6. Cavoretto, R. & De Rossi, A. & Perracchione, E., 2023. "Learning with Partition of Unity-based Kriging Estimators," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    7. Axel Schaffer & Jan Rauland, 2011. "Regional efficiency in generating technological knowledge," ERSA conference papers ersa10p1108, European Regional Science Association.

    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:55:y:2011:i:11:p:2962-2974. 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.