Empirical Bayes Small-Area Estimation Using Logistic Regression Models and Summary Statistics
AbstractMany available methods for estimating small area parameters are model-based, where auxiliary variables are used to predict the variable of interest. For nonlinear models, prediction is not straightforward. MacGibbon and Tomberlin (1989) and Farrell, MacGibbon, and Tomberlin (1994) have proposed methods which require micro-data for each individual in a small area. Here, the authors use a second-order Taylor series expansion to obtain model-based predictions which only require local area summary statistics in the case of either continuous or categorical auxiliary variables. The methodology is evaluated using U.S. census data.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 15 (1997)
Issue (Month): 1 (January)
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Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
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- Alex Costa & Albert Satorra & Eva Ventura, 2003. "An Empirical Evaluation of Five Small Area Estimators," General Economics and Teaching 0312003, EconWPA.
- Àlex Costa & Albert Satorra & Eva Ventura, 2001. "Estimadores compuestos en estadística regional: aplicación para la tasa de variación de la ocupación en la industria," Economics Working Papers 590, Department of Economics and Business, Universitat Pompeu Fabra.
- Àlex Costa & Albert Satorra & Eva Ventura, 2003. "An empirical evaluation of small area estimators," Economics Working Papers 674, Department of Economics and Business, Universitat Pompeu Fabra, revised Jun 2003.
- Àlex Costa & Albert Satorra & Eva Ventura, 2003. "Using composite estimators to improve both domain and total area estimation," Economics Working Papers 731, Department of Economics and Business, Universitat Pompeu Fabra.
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