Robust Small Area Estimation under Spatial Non-stationarity
نویسندگان
چکیده
منابع مشابه
Small area estimation under spatial nonstationarity
In this paper a geographical weighted pseudo empirical best linear unbiased predictor (GWEBLUP) for small area averages is proposed, and two approaches for estimating its mean squared error (MSE), a conditional approach and an unconditional one, are developed. The popular empirical best linear unbiased predictor (EBLUP) under the linear mixed model and its associated MSE estimator are obtained ...
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Small area estimators are often based on linear mixed models under the assumption that relationships among variables are stationary across the area of interest (Fay–Herriot models). This hypothesis is patently violated when the population is divided into heterogeneous latent subgroups. In this paperwe propose a local Fay–Herriotmodel assisted by a SimulatedAnnealing algorithm to identify the la...
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Small area estimation has received considerable attention in recent years because of an increasing demand for small area statistics. Basic area level and unit level models have been studied in the literature to obtain empirical best linear unbiased predictors for small area means. Although this classical method is useful for estimating the small area means efficiently under strict model assumpt...
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We derive a class of composite estimators of small-area quantities that exploit spatial (distance-related) similarity. They are based on a distribution-free model for the areas, but the estimators are aimed to have optimal design-based properties. Composition is applied also to estimating some of the global parameters on which the small-area estimators depend. We show that the commonly adopted ...
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ژورنال
عنوان ژورنال: International Statistical Review
سال: 2018
ISSN: 0306-7734
DOI: 10.1111/insr.12245