This paper deals with the matter of applying a geoadditive model to produce estimates for some geographical domains in the absence of point referenced geographical data. The implementation of a geoadditive model needs the statistical units to be referenced at point locations and if we use it to produce model-based estimates of a parameter of interest for some geographical domains, the spatial location is required for all the population units. This information is not always easily available. Typically, we know the coordinates for sampled units, but for the non-sampled units we only know the areas - like blocks, municipalities, etc. - to which they belong. In such situation, the classic approach is to locate all the non-sampled units by the coordinates of their corresponding area centroid. This is obviously an approximation and its effect on the estimates can be strong, depending on the level of nonlinearity in the spatial pattern and on the area dimension. We propose a different approach that, instead of using the same coordinates for all the units, imposes a distribution for the locations inside each area. Our approach is formalized under a Bayes inferential perspective and its performance is evaluated through various Markov Chain Monte Carlo experiments implemented under different scenarios.

Estimates for geographical domains through geoadditive models in presence of missing geographical information / C. Bocci; E. Rocco. - ELETTRONICO. - 2011/01:(2011), pp. 1-23.

Estimates for geographical domains through geoadditive models in presence of missing geographical information

BOCCI, CHIARA;ROCCO, EMILIA
2011

Abstract

This paper deals with the matter of applying a geoadditive model to produce estimates for some geographical domains in the absence of point referenced geographical data. The implementation of a geoadditive model needs the statistical units to be referenced at point locations and if we use it to produce model-based estimates of a parameter of interest for some geographical domains, the spatial location is required for all the population units. This information is not always easily available. Typically, we know the coordinates for sampled units, but for the non-sampled units we only know the areas - like blocks, municipalities, etc. - to which they belong. In such situation, the classic approach is to locate all the non-sampled units by the coordinates of their corresponding area centroid. This is obviously an approximation and its effect on the estimates can be strong, depending on the level of nonlinearity in the spatial pattern and on the area dimension. We propose a different approach that, instead of using the same coordinates for all the units, imposes a distribution for the locations inside each area. Our approach is formalized under a Bayes inferential perspective and its performance is evaluated through various Markov Chain Monte Carlo experiments implemented under different scenarios.
2011
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/406411
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