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Activity Number:
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537
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #306850 |
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Title:
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Using Regression To Combine Information from Multiple Surveys for Small-Domain Estimation
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Author(s):
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Takis Merkouris*+
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Companies:
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Statistics Canada
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Address:
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R.H. Coats Building, 16th Floor, Ottawa, ON, K1A 0T6, Canada
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Keywords:
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small area ; rare characteristics ; generalized regression estimator ; calibration ; composite estimator
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Abstract:
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The possibility of enhancing the efficiency of domain estimators by combining comparable information collected in multiple surveys of the same population has been pointed out in recent literature, but it has not been explored to date. We propose a regression method of estimation that is essentially an extended calibration procedure whereby comparable domain estimates from the various surveys are calibrated to each other. We show through analytic results and an empirical study that this method may greatly improve the efficiency of domain estimators for the variables that are common to these surveys, as these estimators make effective use of increased sample size for the common survey items. The proposed approach is equally suitable for small geographic and non-geographic domains. It is also highly effective in handling the closely related problem of estimation for rare characteristics.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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