On dispersion preserving estimation of the mean of a binary variable from small areas
AbstractOver-shrinkage is a common problem in small area (or domain) estimation. It happens when the estimated small-area parameters have less between-area variation than their true values. To deal with this problem, Louis (1984), Ghosh (1992) and Spjøtvoll and Thomsen (1987) have proposed various constrained empirical and hierarchical Bayes methods. In this paper we study two non-Bayesian methods based on, respectively, the synthetic estimator and a variance-component model. We show first that the synthetic estimator entails loss of dispersion in general, from which it follows that the coverage level of the confidence intervals could be far below the nominal level of confidence, when these are derived from the sampling error alone. A bivariate variance-component model at the area-level, as well as its simplification, can greatly improve the efficiency of the confidence intervals. However, super-population approaches as such are unable to capture the distribution of the true area-parameters. We develop a finite-population approach based on an empirical finite-population distribution function of the area-parameters, which provides the necessary adjustment. The various methods will be illustrated using the data of the Census 1990. Finally, we notice that several European countries will base the upcoming Census on their administrative register systems, instead of collecting the information in the field. Improved small area estimation methods may prove to be valuable for assessing the quality of such Register Counting.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Research Department of Statistics Norway in its series Discussion Papers with number 285.
Date of creation: Aug 2000
Date of revision:
Over-shrinkage; synthetic estimator; variance-component model;
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (J Bruusgaard).
If references are entirely missing, you can add them using this form.