IDEAS home Printed from https://ideas.repec.org/p/ags/hebarc/18582.html
   My bibliography  Save this paper

Kriging with Nonparametric Variance Function Estimation

Author

Listed:
  • Opsomer, Jean D.
  • Ruppert, D.
  • Wand, M.P.
  • Holst, U.
  • Hossjer, O.

Abstract

A method for fitting regression models to data that exhibit spatial correlation and Heteroskedasticity is proposed. A combination of parametric and nonparametric regression techniques is used to iteratively estimate the various components of the model. The approach is demonstrated on a large dataset of predicted nitrogen runoff from agricultural lands in the Midwest and Northern Plains regions of the U.S. For this dataset, the model is comprised of three main components: (1) the mean function which includes farming practice variables, local soil and climate characteristics and the nitrogen application treatment, is assumed linear in the parameters and fitted by generalized least squares. (2) the variance function, which contains a local as well as a spatial component whose shapes are left unspecified, is estimated by local as well as a spatial component whose shapes are left unspecified, is estimated by local linear regression, and (3) the spatial correlation function is estimated by fitting parametric variogram model to the standardized residuals and after adjusting the variogram for the presence of Heteroskedasticity. The fitting of these three components is iterated until convergence. The model provides and improved fit to the data compared to a previous model that ignored the Heteroskedasticity and the spatial correlation.

Suggested Citation

Handle: RePEc:ags:hebarc:18582
DOI: 10.22004/ag.econ.18582
as

Download full text from publisher

File URL: https://ageconsearch.umn.edu/record/18582/files/wp980188.pdf
Download Restriction: no
File URL: https://libkey.io/10.22004/ag.econ.18582?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
---><---

More about this item

Keywords

;

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:ags:hebarc:18582. 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.

We have no bibliographic references for this item. You can help adding them by using 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: AgEcon Search (email available below). General contact details of provider: .

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.