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Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates | | Aaron T. Porter
; Scott H. Holan
; Christopher K. Wikle
; Noel Cressie
; | | Date: |
26 Mar 2013 | | Abstract: | The Fay-Herriot (FH) model is widely used in small area estimation and uses
auxiliary information to reduce estimation variance at undersampled locations.
We extend the type of covariate information used in the FH model to include
functional covariates, such as social-media search loads, or remote-sensing
images (e.g., in crop-yield surveys). The inclusion of these functional
covariates is facilitated through a two-stage dimension reduction approach that
includes a Karhunen Lo’{e}ve expansion followed by stochastic search variable
selection. Additionally, the importance of modeling spatial autocorrelation has
recently been recognized in the FH model; our model utilizes the conditional
autoregressive class of spatial models in addition to functional covariates. We
demonstrate the effectiveness of our approach through simulation and through
the analysis of American Community Survey data. We use Google Trends search
curves as functional covariates to analyze changes in rates of household
Spanish speaking in the eastern half of the United States. | | Source: | arXiv, 1303.6668 | | Services: | Forum | Review | PDF | Favorites | |
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