Empirical Best Linear Unbiased Prediction for Out of Sample Areas

Saei, Ayoub and Chambers, Ray (2005) Empirical Best Linear Unbiased Prediction for Out of Sample Areas. Southampton, UK, Southampton Statistical Sciences Research Institute, 15pp. (S3RI Methodology Working Papers, M05/03).


[img] PDF - Author's Original
Download (260Kb)


Models for small area estimation based on a random effects specification typically assume population units in different areas are uncorrelated. However, they can be extended to account for the correlation between areas by assuming that area random effects are spatially correlated. In this paper we suggest a simple variance-covariance structure for such a spatial correlation structure within the context of a linear model for the population characteristic of interest, and derive estimates of parameters and components of variance using maximum likelihood and restricted maximum likelihood methods. This allows empirical best linear unbiased predictions for area totals to be computed for areas in sample as well as those that are not in sample. An expression for the mean cross-product error (MCPE) matrix of these predicted small area totals is derived, as is an estimator of this matrix. The estimation approach described in the paper is then evaluated by a simulation study, which compares the new method with other methods of small area estimation for this situation.

Item Type: Monograph (Working Paper)
Subjects: H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 14073
Accepted Date and Publication Date:
25 January 2005Made publicly available
Date Deposited: 26 Jan 2005
Last Modified: 31 Mar 2016 11:26
URI: http://eprints.soton.ac.uk/id/eprint/14073

Actions (login required)

View Item View Item

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics