Krige, smooth, both or neither? (with Discussion)

Australian and New Zealand Journal of Statistics

Volumn 42, Issue 4, 2000, Pages 441-461

  Altman, Naomi ^a  

^a Cornell University ^*  (United States)

Author keywords

Generalized cross validation; Interpolation; Non parametric regression; Non stationary processes; Prediction; Smoothing spline; Spatial statistics

Indexed keywords

EID: 0034367971     PISSN: 13691473     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-842X.00141     Document Type: Article

References (25)

1. ALTMAN, N.S. (1990). Kernel smoothing of data with correlated errors. J. Amer. Statist. Assoc. 85, 749-759.
2. ALTMAN, N. S. (1992). An iterated Cochrane-Orcutt procedure for non-parametric regression. J. Statist. Comput. Simulation 40, 93-108.
3. BREAKER, L.C. & LEWIS, P.A.W. (1988). A 40-50 day oscillation in sea-surface temperature along the central California coast. Estuarine Coast and Shelf Science 26, 395-408.
4. CHIU, S. (1989). Bandwidth selection for kernel estimate with correlated noise. Statist. Probab. Lett. 8, 347-354.
5. CHU, C-K. & MARRON, J.S. (1991). Comparison of two bandwidth selectors with dependent errors. Ann. Statist. 19, 1906-1918.
6. CRAVEN, P. & WAHBA, G. (1979). Smoothing noisy data with spline functions. Numer. Math. 31, 377-403.
7. CRESSIE, N. (1989). Geostatistics. Amer. Statist. 43, 256-258.
8. CRESSIE, N. (1993). Statistics for Spatial Data, revised edition. New York: John Wiley & Sons.
9. DIGGLE, P.G. & HUTCHINSON, M.F. (1989). On spline smoothing with autocorrelated errors. Aust. J. Statist. 31, 166-182.
10. ENGLE, R., GRANGER, C.W.J., RICE, J. & WEISS, A. (1986). Semiparametric estimates of the relation between weather and electricity sales. J. Amer. Statist. Assoc. 81, 310-320.
11. EUBANK, R.L. (1988). Spline Smoothing and Non-parametric Regression. New York: Marcel Dekker, Inc..
12. HART, J.D. (1991). Kernel regression with time series errors. J. R. Stat. Soc. Ser. B Stat. Methodol. 53, 173-187.
13. HERRMANN, E., GASSER, T. & KNEIP, A. (1992). Choice of bandwidth for kernel regression when residuals are correlated. Biometrika 79, 783-795.
14. HOBERT, J.P., ALTMAN, N.S. & SCHOFIELD, C.L. (1997). Analyses of fish species richness with spatial covariate. J. Amer. Statist. Assoc. 92, 846-854.
15. HUTCHINSON, M.F. & GESSLER, F.R. (1994). Splines - more than just a smooth interpolator. Geoderma 62, 45-67.
16. LASLETT, G.M. (1994). Kriging and splines: an empirical comparison of their predictive performance in some applications (with discussion). J. Amer. Statist. Assoc. 89, 392-409.
17. MÜLLER, H-G. & STADTMÜLLER, U. (1988). Detecting dependencies in smooth regression models. Biometrika 75, 639-650.
18. OPSOMER, J. (1996). Estimating an unknown function by local linear regression when the errors are correlated. Preprint 95-42. Iowa State University.
19. SEARLE, S.R., CASELLA, G. & McCULLOCH, C.E. (1992). Variance Components. New York: John Wiley & Sons.
20. TRUONG, Y.K. (1991). Non-parametric curve estimation with time series errors. J. Statist. Plann. Inference 28, 167-183.
21. VOLTZ, M. & WEBSTER, R. (1990). A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. J. Soil Sci. 41, 473-490.
22. WAHBA, G. (1990). Comment on Cressie. Amer. Statist. 94, 255-256.
23. WAND, M.P. & JONES, M.C. (1995). Kernel Smoothing. London: Chapman and Hall.
24. WANG, Y. (1998). Smoothing spline models with correlated random errors. J. Amer. Statist. Assoc. 93, 341-348.
25. YAKOWITZ, S.J. & SZIDAROVSKY, F. (1985). A comparison of kriging with non-parametric regression methods. J. Multivariate Anal. 16, 21-53.