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Activity Number:
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18
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Type:
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Contributed
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Date/Time:
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Sunday, August 11, 2002 : 2:00 PM to 3:50 PM
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Sponsor:
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General Methodology
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| Abstract - #301866 |
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Title:
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Assessment of the Robustness of the Quadratic Inference Function
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Author(s):
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Annie Qu*+
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Affiliation(s):
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Oregon State University
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Address:
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44 Kidder Hall, Corvallis, Oregon, 97331, USA
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Keywords:
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Longitudinal data ; robustness ; consistency ; quadratic inference function ; data contamination
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Abstract:
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The quadratic inference function (QIF) was proposed by Qu et al. (2000) for analyzing correlated data. The QIF is built on a semiparametric framework defined by a set of mean zero estimating functions, but differs from the standard estimating function approach in that there are more equations than the number of unknown parameters. The QIF has advantages of the estimating function approach such as not requiring the specification of the likelihood function; but also overcomes limitations of the estimating function approach such as a lack of objective functions and likelihood functions for testing. In this talk, we will explore the QIF for robustness with respect to the consistency of estimators when model assumptions are not satisfied. Our preliminary results indicate that the QIF's estimator is robust against data contamination, and this can be explained by the advantage of using a distance function as the objective function in the QIF approach.
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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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