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221013 ||| eng |
| 100 |
1 |
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|a Elbers, Chris
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| 245 |
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|a Estimation of Normal Mixtures in a Nested Error Model with an Application to Small Area Estimation of Poverty and Inequality
|h Elektronische Ressource
|c Elbers, Chris
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| 260 |
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|a Washington, D.C
|b The World Bank
|c 2014
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| 300 |
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|a 33 p
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| 700 |
1 |
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|a Elbers, Chris
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| 700 |
1 |
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|a van der Weide, Roy
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b WOBA
|a World Bank E-Library Archive
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| 028 |
5 |
0 |
|a 10.1596/1813-9450-6962
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| 856 |
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|u http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-6962
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a This paper proposes a method for estimating distribution functions that are associated with the nested errors in linear mixed models. The estimator incorporates Empirical Bayes prediction while making minimal assumptions about the shape of the error distributions. The application presented in this paper is the small area estimation of poverty and inequality, although this denotes by no means the only application. Monte-Carlo simulations show that estimates of poverty and inequality can be severely biased when the non-normality of the errors is ignored. The bias can be as high as 2 to 3 percent on a poverty rate of 20 to 30 percent. Most of this bias is resolved when using the proposed estimator. The approach is applicable to both survey-to-census and survey-to-survey prediction
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