论文信息 - Testing for Simplification in Spatial Models

Testing for Simplification in Spatial Models

Data collected on a rectangular lattice occur frequently in many areas such as field trials, geostatistics, remotely sensed data, and image analysis. Models for the spatial process often make simplifying assumptions, including axial symmetry and separability. We consider methods for testing these assumptions and compare tests based on sample covariances, tests based on the sample spectrum, and model-based tests.

Luisa Scaccia | R. J. Martin

[1] N. Balram,et al. Noncausal Gauss Markov random fields: Parameter structure and estimation , 1993, IEEE Trans. Inf. Theory.

[2] J. O. Rawlings,et al. Spatial Correlation Analysis of Uniformity Data , 1983 .

[3] Mike Rees,et al. 5. Statistics for Spatial Data , 1993 .

[4] D. K. Pickard,et al. Unilateral Gaussian fields , 1992, Advances in Applied Probability.

[5] D. B. Preston. Spectral Analysis and Time Series , 1983 .

[6] D. K. Pickard,et al. Unilateral Markov fields , 1980, Advances in Applied Probability.

[7] R. J. Martin. A subclass of lattice processes applied to a problem in planar sampling , 1979 .

[8] R. J. Martin. The use of time-series models and methods in the analysis of agricultural field trials , 1990 .

[9] Lynne Billard,et al. Some Inference Results for Causal Autoregressive Processes on a Plane , 1998 .

[10] Noel A Cressie,et al. Statistics for Spatial Data. , 1992 .

[11] R. J. Martin. Some results on unilateral ARMA lattice processes , 1996 .