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Abstract Details
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Activity Number:
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630
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #301219 |
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Title:
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Efficient Estimation with Missing Responses
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Author(s):
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Scott Daniel Crawford*+
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Companies:
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Texas A & M University
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Address:
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1204 Anna Street, College Station, TX, 77840,
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Keywords:
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regression ;
imputation ;
efficiency
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Abstract:
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Many competing methods have been proposed to deal with regression models that have data missing at random. These methods often require knowledge of the error distribution. This research examines the methods that can be used when the error distribution is unknown, and which of these methods are efficient. Theoretical results and simulations highlight the benefits and drawbacks of each method. The focus is on estimating the regression parameter, and the mean of a possibly missing response variable. The analysis shows under which scenarios the Ordinary Least Squares is least dispersed, and when Full Imputation is the only method that allows efficient estimation. Using Full Imputation the missing responses are imputed, as well as the responses that are not missing.
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