Chalmers R Philip
Department of Educational Psychology, University of Georgia, Athens, Georgia, USA.
Br J Math Stat Psychol. 2018 Nov;71(3):415-436. doi: 10.1111/bmsp.12127. Epub 2018 Jan 9.
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.
提出了一种高效且准确的数值近似方法，该方法在使用期望最大化（EM）算法拟合模型时，有助于获得观测信息矩阵及后续的渐近协方差矩阵。将该数值近似方法与用于相同目的的现有算法进行了比较，并突出了这种新方法的计算优势和准确性。文中包含了具有启发性的实际示例，以具体展示该方法，详细讨论了估计量的性质，还纳入了一项蒙特卡罗模拟研究，以使用三种相互竞争的有限差分算法来研究多参数项目反应理论模型的行为。
