This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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304
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Government Statistics
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Abstract - #307571 |
Title:
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Sharing Precise Geographies in Public Use Data
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Author(s):
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Hao Wang* and Jerome P. Reiter+
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Companies:
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Duke University and Duke University
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Address:
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112A Old Chemistry Bldg, Durham, NC, 27708-0251,
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Keywords:
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Confidentiality ;
Disclosure ;
Geography ;
Multiple Imputation ;
Spatial Analysis
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Abstract:
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When statistical agencies release data to the public, they are obligated by law to protect the confidentiality of survey respondents' identities. Geographical variables are among the most challenging data to share when making a primary data source available to public. In this paper, we develop a multiple imputation approach for protecting the confidentiality of data with geographic identifiers. We propose the use of a sequence of classification and regression trees to generate synthetic geography that can preserve spatial associations. We then describe a novel disclosure risk measure in the multiply-imputed data sets, accounting for released information from the multiple datasets. Finally, we illustrate the computation using the Durham city mortality data set.
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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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