Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing
نویسندگان
چکیده
منابع مشابه
Smoothed Anova with Spatial Effects as a Competitor to Mcar in Multivariate Spatial Smoothing.
Rapid developments in geographical information systems (GIS) continue to generate interest in analyzing complex spatial datasets. One area of activity is in creating smoothed disease maps to describe the geographic variation of disease and generate hypotheses for apparent differences in risk. With multiple diseases, a multivariate conditionally autoregressive (MCAR) model is often used to smoot...
متن کاملSmoothed Anova with Spatial Effects as a Competitor to Mcar in Multivariate Spatial Smoothing By
Rapid developments in geographical information systems (GIS) continue to generate interest in analyzing complex spatial datasets. One area of activity is in creating smoothed disease maps to describe the geographic variation of disease and generate hypotheses for apparent differences in risk. With multiple diseases, a multivariate conditionally autoregressive (MCAR) model is often used to smoot...
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A new method, smoothing spline ANOVA, for combining station records of surface air temperature to get the estimates of regional averages as well as gridpoint values is proposed. This method is closely related to the optimal interpolation (also optimal averaging) method. It may be viewed as a generalization of these methods from spatial interpolation methods to a method interpolating in both spa...
متن کاملSpatial Smoothing
The size of the Gaussian kernel defines the "width" of the curve which determines in turn how much the data is smoothed. The width is not expressed in terms of the standard deviation ?, as customary in statistics, but with the Full Width at Half Maximum (FWHM). In this case the FWHM would be 2.35: The maximum of this curve is y = 0.4 at x = 0. The half maximum is y = 0.2 at x = -1.175 and at x ...
متن کاملSpatial Smoothing
The size of the Gaussian kernel defines the "width" of the curve which determines in turn how much the data is smoothed. The width is not expressed in terms of the standard deviation ?, as customary in statistics, but with the Full Width at Half Maximum (FWHM). In this case the FWHM would be 2.35: The maximum of this curve is y = 0.4 at x = 0. The half maximum is y = 0.2 at x = -1.175 and at x ...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2009
ISSN: 1932-6157
DOI: 10.1214/09-aoas267