This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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483
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #306747 |
Title:
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Balanced Random Imputation for Estimating Coefficients of Correlations in Surveys
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Author(s):
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David Haziza*+ and Guillaume Chauvet
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Companies:
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Université de Montréal and ENSAI
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Address:
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2920, chemin de la Tour, bureau 5190, Montreal, QC, H3T 1J4, Canada
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Keywords:
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Coefficient of correlation ;
Marginal imputation ;
item nonresponse ;
Random balanced imputation
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Abstract:
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Marginal imputation that consists of imputing items separately, generally leads to biased estimators of population coefficients of correlation. To overcome this problem Shao and Wang (2002) proposed a joint random regression imputation method that succeeds in preserving the relationships between two variables. One drawback of the Shao-Wang method is that it introduces an additional amount of variability (called the imputation variance) due to the random selection of residuals. As a result, it could lead to inefficient estimators. Following Chauvet, Deville and Haziza (2009), we propose a balanced joint random regression imputation that preserves the coefficient of correlation between two variables, while virtually eliminating the imputation variance. Results of a simulation study will be presented.
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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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