Small Area Estimation with Skewed Data
Small Area Estimation with Skewed Data
In business surveys, data typically are skewed and the standard approach for small area estimation based on linear mixed models lead to inefficient estimates. In this paper, we discuss small area estimation techniques for skewed data that are linear following a suitable transformation. In this context, implementation of the empirical best linear unbiased prediction (EBLUP) approach under transformation to a linear mixed model is complicated. However, this is not the case with the model-based direct (MBD) approach (Chambers and Chandra, 2006), which is based on weighted linear estimators. We extend the MBD approach to skewed data using sample weights derived via model calibration based on a log transform model with random area effects. Our results show this estimator is both efficient and robust with respect to the distribution of these random effects. An application to real data demonstrates the satisfactory performance of the method.
Southampton Statistical Sciences Research Institute, University of Southampton
Chandra, Hukum
20235c19-9d73-47d0-abcc-65d9d8cc716c
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
8 June 2006
Chandra, Hukum
20235c19-9d73-47d0-abcc-65d9d8cc716c
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Chandra, Hukum and Chambers, Ray
(2006)
Small Area Estimation with Skewed Data
(S3RI Methodology Working Papers, M06/05)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
24pp.
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Monograph
(Working Paper)
Abstract
In business surveys, data typically are skewed and the standard approach for small area estimation based on linear mixed models lead to inefficient estimates. In this paper, we discuss small area estimation techniques for skewed data that are linear following a suitable transformation. In this context, implementation of the empirical best linear unbiased prediction (EBLUP) approach under transformation to a linear mixed model is complicated. However, this is not the case with the model-based direct (MBD) approach (Chambers and Chandra, 2006), which is based on weighted linear estimators. We extend the MBD approach to skewed data using sample weights derived via model calibration based on a log transform model with random area effects. Our results show this estimator is both efficient and robust with respect to the distribution of these random effects. An application to real data demonstrates the satisfactory performance of the method.
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Published date: 8 June 2006
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Local EPrints ID: 38417
URI: http://eprints.soton.ac.uk/id/eprint/38417
PURE UUID: b9bbd4a1-614f-4c32-9393-bcec9d26f498
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Date deposited: 09 Jun 2006
Last modified: 15 Mar 2024 08:07
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Author:
Hukum Chandra
Author:
Ray Chambers
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