Robust Estimation and Inference for Generalised Latent Trait Models

The paper discusses the effect of model deviations such as data contamination on the maximum likelihood estimator (MLE) for a general class of latent trait models (citeNP{MoKn:00}). This is done with the use of the influence function (Hampel 1968, 1974) a mathematical tool to assess the robustness properties of any statistic, such as an estimator. Simulation studies show that the MLE can be seriously biased by model deviations. Therefore, we then propose alternative robust estimators that are not less influenced by data contamination. The performance of the robust estimators in terms of bias and variance is compared to the MLE estimator both analytically and through simulation studies.