Small area estimation using skew normal models
AbstractTwo connected extensions of the Fay–Herriot small area level model that are of practical and theoretical interest are proposed. The first extension allows for the sampling error to be non-symmetrically distributed. This is important for cases in which the sample sizes in the areas are not large enough to rely on the central limit theorem (CLT). This is dealt with by assuming that the sample error is skew normally distributed. The second extension proposes to jointly model the direct survey estimator and its respective variance estimator, borrowing strength from all areas. In this way, all sources of uncertainties are taken into account. The proposed model has been applied to a real data set and compared with the usual Fay–Herriot model under the assumption of unknown sampling variances. A simulation study was carried out to evaluate the frequentist properties of the proposed model. The evaluation studies show that the proposed model is more efficient for small area predictions under skewed data than the customarily employed normal area model.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 10 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/csda
Bayesian inference; MCMC; Hierarchical models;
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: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.