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Statistical Inference Regularized with Neural Networks and its Application in Psychometrics
Likelihood-based methods such as marginal maximum likelihood and Bayesian estimation via Markov chain Monte Carlo (MCMC) are widely used for estimating item response theory (IRT) models and perform well in large samples. In small samples, however, these approaches may yield unstable estimates due to
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