Activity Number:
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6
- Learning from Permuted Data and the Analysis of Linked Files
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Government Statistics Section
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Abstract #309285
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Title:
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Use of Probabilistic Record Linkage in Multiple-Frame Surveys
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Author(s):
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Partha Lahiri and Takumi Saegusa*
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Companies:
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University of Maryland, College Park and University of Maryland, College Park
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Keywords:
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Combining surveys;
nonsamplig errors;
survey costs
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Abstract:
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There is a growing interest in using multiple-frame surveys in recent years in order to save survey costs and reduce different types of nonsampling errors. Following the pioneering work by Hartley, methods and theories have been developed in a number of important papers. But all of the papers in the literature rely on a strong assumption that domain membership of each unit of the finite population is known. This assumption is hardly met in practice. The effect of violation of this critical assumption on finite population inference is not fully understood. We first investigate the effect of misspecification of the domain membership on estimation and variance estimation. We then exploit the recent development of probabilistic record linkage techniques in adjusting for biases due to domain membership misspecification in finite population inference. We study the properties of the proposed estimators and the associated variance estimators analytically and through Monte Carlo simulations.
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Authors who are presenting talks have a * after their name.