Statistical inference and visualization in scale-space for spatially dependent images
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/599729
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AbstractSiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for statistical inferences. In this paper we develop a spatial SiZer for finding significant features and conducting goodness-of-fit tests for spatially dependent images. The spatial SiZer utilizes a family of kernel estimates of the image and provides not only exploratory data analysis but also statistical inference with spatial correlation taken into account. It is also capable of comparing the observed image with a specific null model being tested by adjusting the statistical inference using an assumed covariance structure. Pixel locations having statistically significant differences between the image and a given null model are highlighted by arrows. The spatial SiZer is compared with the existing independent SiZer via the analysis of simulated data with and without signal on both planar and spherical domains. We apply the spatial SiZer method to the decadal temperature change over some regions of the Earth. © 2011 The Korean Statistical Society.
CitationVaughan A, Jun M, Park C (2012) Statistical inference and visualization in scale-space for spatially dependent images. Journal of the Korean Statistical Society 41: 115–135. Available: http://dx.doi.org/10.1016/j.jkss.2011.07.006.
SponsorsThis work is part of the first author's dissertation. Mikyoung Jun acknowledges support from NSF grants ATM-0620624 and DMS-0906532. Mikyoung Jun's research is partially supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors acknowledge the modeling groups for making their simulation available for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP3 model output, and the World Climate Research Programme (WCRP)'s Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The WCRP CMIP3 multi-model data set is supported by the Office of Science, US Department of Energy.