Abstract Details
Activity Number:
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138
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Survey Research Methods Section
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Abstract - #309689 |
Title:
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Likelihood-Based Finite Sample Inference Based on Synthetic Data
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Author(s):
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Bimal Sinha*+
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Companies:
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UMBC
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Keywords:
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synthetic data ;
likelihood based analysis ;
noise multiplication
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Abstract:
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When statistical agencies release micro data to the public, a major concern is the control of disclosure risk while ensuring utility in the released data. We consider likelihood based finite sample inference based on synthetic data for three probability models: exponential with an unknown mean, and normal with an unknown mean and either known or unknown variance, leading to some new results and conclusions! A comparison of this analysis with the one under noise multiplication also reveals some interesting features.
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Authors who are presenting talks have a * after their name.
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