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Small area estimation under a spatially non-linear model

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Title Small area estimation under a spatially non-linear model
Not Available
 
Creator Hukum Chandra
Nicola Salvati
Ray Chambers
 
Subject Small area estimation
Nonparametric models
Spatial relationship
Count data
Poverty indicator
 
Description Not Available
We describe a methodology for small area estimation of counts that assumes an arealevel
version of a nonparametric generalized linear mixed model with a mean structure
defined using spatial splines. The proposed method represents an alternative to other small
area estimation methods based on area level spatial models that are designed for both
spatially stationary and spatially non-stationary populations. We develop an estimator for
the mean squared error of the proposed small area predictor as well as an approach for
testing for the presence of spatial structure in the data and evaluate both the proposed
small area predictor and its mean squared error estimator via simulations studies. Our empirical results show that when data are spatially non-stationary the proposed small area
predictor outperforms other area level estimators in common use and that the proposed
mean squared error estimator tracks the actual mean squared error reasonably well, with
confidence intervals based on it achieving close to nominal coverage. An application to
poverty estimation using household consumer expenditure survey data from 2011–12
collected by the national sample survey office of India is presented.
Not Available
 
Date 2018-10-19T09:34:54Z
2018-10-19T09:34:54Z
2018-04-25
 
Type Research Paper
 
Identifier Hukum Chandra.,Nicola Salvati and RayChambers (2018). Small area estimation under a spatially non-linear model. Computational Statistics & Data Analysis, 126, 19-38.
Not Available
https://doi.org/10.1016/j.csda.2018.04.002
http://krishi.icar.gov.in/jspui/handle/123456789/8016
 
Language English
 
Relation Computational Statistics and Data Analysis;126 (2018)
 
Publisher Elsevier B.V