Variance Estimation in Spatial Regression Using a Non-parametric Semivariogram Based on Residuals
The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor effect because the bias is small for small lags. However, for estimating the variance of estimated regression coefficients and of predictions, the bias due to using residuals can be quite substantial. Thus we propose a method for reducing this bias. The adjusted empirical semivariogram is then isotonized and made conditionally negative-definite and used to estimate the variance of estimated regression coefficients in a general estimating equations setup. Simulation results for least squares and robust regression show that the proposed method works well in linear models with stationary correlated errors. Copyright 2004 Board of the Foundation of the Scandinavian Journal of Statistics..
Year of publication: |
2004
|
---|---|
Authors: | Kim, Hyon-Jung ; Boos, Dennis D. |
Published in: |
Scandinavian Journal of Statistics. - Danish Society for Theoretical Statistics, ISSN 0303-6898. - Vol. 31.2004, 3, p. 387-401
|
Publisher: |
Danish Society for Theoretical Statistics Finnish Statistical Society Norwegian Statistical Association Swedish Statistical Association |
Saved in:
freely available
Saved in favorites
Similar items by person
-
Spatial modeling with spatially varying coefficient processes
Gelfand, Alan E., (2003)
-
Predicting spatial patterns of house prices using LPR and Bayesian smoothing
Clapp, John M., (2002)
-
Spatial Prediction of House Prices Using Lpr and Bayesian Smoothing
Clapp, John M., (2001)
- More ...