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Abstract Details
Activity Number:
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354
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #304475 |
Title:
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Asymptotic Expansions for the Pivots Using Log-Likelihood Derivatives with an Application in Item Response Theory
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Author(s):
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Haruhiko Ogasawara*+
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Companies:
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Otaru University of Commerce
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Address:
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3 Chome 5 Banchi 21 Midori, Otaru, 047-8501, , Japan
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Keywords:
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pivots ;
log-likelihood derivatives ;
inverse expansion ;
sandwich estimator ;
item response theory
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Abstract:
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Asymptotic expansions of the distributions of the pivotal statistics involving log-likelihood derivatives under possible model misspecification are derived using the asymptotic cumulants up to the fourth order and the higher-order asymptotic variance. The pivots dealt with are the studentized ones by the estimated expected information, the negative Hessian matrix, the sum of products of gradient vectors, and the so-called sandwich estimator. It is shown that the first three asymptotic cumulants are the same over the pivots under correct model specification with a general condition of the equalities. An application is given in item response theory, where the observed information is usually used rather than the estimated expected one.
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Authors who are presenting talks have a * after their name.
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