An Iterative Maximum a Posteriori Estimation of Proficiency Level to Detect Multiple Local Likelihood Maxima

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Title: An Iterative Maximum a Posteriori Estimation of Proficiency Level to Detect Multiple Local Likelihood Maxima
Language: English
Authors: Magis, David, Raiche, Gilles
Source: Applied Psychological Measurement. 2010 34(2):75-89.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Peer Reviewed: Y
Physical Description: PDF
Page Count: 15
Publication Date: 2010
Document Type: Journal Articles
Reports - Research
Descriptors: Maximum Likelihood Statistics, Computation, Bayesian Statistics, Item Response Theory, Simulation
DOI: 10.1177/0146621609336540
ISSN: 0146-6216
Abstract: In this article the authors focus on the issue of the nonuniqueness of the maximum likelihood (ML) estimator of proficiency level in item response theory (with special attention to logistic models). The usual maximum a posteriori (MAP) method offers a good alternative within that framework; however, this article highlights some drawbacks of its use. The authors then propose an iteratively based MAP estimator (IMAP), which can be useful in detecting multiple local likelihood maxima. The efficiency of the IMAP estimator is studied and is compared to the ML and MAP methods by means of a simulation study. (Contains 4 tables and 2 figures.)
Abstractor: As Provided
Number of References: 10
Entry Date: 2010
Accession Number: EJ874526
Database: ERIC
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  Data: In this article the authors focus on the issue of the nonuniqueness of the maximum likelihood (ML) estimator of proficiency level in item response theory (with special attention to logistic models). The usual maximum a posteriori (MAP) method offers a good alternative within that framework; however, this article highlights some drawbacks of its use. The authors then propose an iteratively based MAP estimator (IMAP), which can be useful in detecting multiple local likelihood maxima. The efficiency of the IMAP estimator is studied and is compared to the ML and MAP methods by means of a simulation study. (Contains 4 tables and 2 figures.)
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