A comparison of parameter covariance estimation methods for item response models in an expectation-maximization framework
The Expectation-Maximization (EM) algorithm is a method for finding the maximum likelihood estimate of a model in the presence of missing data. Unfortunately, EM does not produce a parameter covariance matrix for standard errors. Both Oakes and Supplemented EM are methods for obtaining the parameter...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-12-01
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| Series: | Cogent Psychology |
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| Online Access: | http://dx.doi.org/10.1080/23311908.2017.1279435 |