Variogram estimation in the presence of trend

Statistics and its Interface

Volumn 5, Issue 2, 2012, Pages 159-168

  Bliznyuk, Nikolay ^a   Carroll, Raymond J ^b   Genton, Marc G ^b   Wang, Yuedong ^c  

^a University of Florida   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bias; Covariance function; Nonlinear regression; Nonparametric regression; Spatio temporal dependence; Time series

Indexed keywords

EID: 84864392037     PISSN: 19387989     EISSN: 19387997     Source Type: Journal    
DOI: 10.4310/SII.2012.v5.n2.a2     Document Type: Article

References (15)

1. Apanasovich, T. V. and Genton, M. G. (2010). Cross-covariance functions for multivariate random fields based on latent dimensions, Biometrika 97, 15-30
2. Banerjee, S., Carlin, B. P., and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Boca Raton: Chapman and Hall-CRC.
3. Cressie, N. (1993). Statistics for Spatial Data. Chichester: John Wiley and; Sons
4. Francisco-Fernandez, M. and Opsomer, J. D. (2005). Smoothing parameter selection methods for nonparametric regression with spatially correlated errors, The Canadian Journal of Statistics 33, 279-295
5. Furrer, R., Genton, M. G., and Nychka, D. (2006). Covariance tapering for interpolation of large spatial datasets, Journal of Computational and Graphical Statistics 15, 502-523
6. Gneiting, T., Genton, M. G., and Guttorp, P. (2007). Geostatistical space-time models, stationarity, separability and full symmetry, in Statistics of Spatio-Temporal Systems, B. Finkenstaedt, L. Held, V. Isham (eds), Chapman and; Hall/CRC Press, Monographs in Statistics and Applied Probability, pp. 151-175.
7. Hall, P. and Van Keilegom, I. (2003). Using difference-based methods for inference in nonparametric regression with time series errors, Journal of the Royal Statistical Society, Series B 65, 443- 456
8. Johns, C., Nychka, D., Kittel, T., and Daly, C. (2003). Infilling sparse records of spatial fields, Journal of the American Statistical Association 98, 796-806
9. Lahiri, S. N., Lee, Y., and Cressie, N. (2002). On asymptotic distribution and asymptotic effciency of least squares estimators of spatial variogram parameters, Journal of Statistical Planning and Inference 103, 65-85
10. Müller, W. G. (1999). Least-squares fitting from the variogram cloud, Statistics and; Probability Letters 43, 93-98
11. Opsomer, J., Wang, Y., and Yang, Y. (2001). Nonparametric regression with correlated errors, Statistical Science 16, 134-153
12. Ramsey, J. O. and Silverman, B. W. (2005). Functional Data Analysis, 2nd ed., New York: Springer-Verlag
13. Santner, T. J., Williams, B. J., and Notz, W. (2003). The Design and Analysis of Computer Experiments. New York: Springer-Verlag
14. Sun, Y., Li, B., and Genton, M. G. (2012). Geostatistics for large datasets, in Space-Time Processes and Challenges Related to Environmental Problems, E. Porcu, J. M. Montero, M. Schlather (eds), Chapter 3, Springer Lecture Notes in Statistics, Vol. 207, to appear.
15. Tong, T. and Wang, Y. (2005). Estimating residual variance in nonparametric regression using least squares, Biometrika 92, 821-830