Journal
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY
Volume 71, Issue 3, Pages 415-436Publisher
WILEY
DOI: 10.1111/bmsp.12127
Keywords
EM algorithm; supplemented EM; observed information; Oakes's identity; item response theory; finite differences
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An efficient and accurate numerical approximation methodology useful for obtaining the observed information matrix and subsequent asymptotic covariance matrix when fitting models with the EM algorithm is presented. The numerical approximation approach is compared to existing algorithms intended for the same purpose, and the computational benefits and accuracy of this new approach are highlighted. Instructive and real-world examples are included to demonstrate the methodology concretely, properties of the estimator are discussed in detail, and a Monte Carlo simulation study is included to investigate the behaviour of a multi-parameter item response theory model using three competing finite-difference algorithms.
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