Bayesian nonparametric (BNP) models are becoming increasingly important in psychology,
both as theoretical models of cognition and as analytic tools. However, existing tutorials tend …

The goal of this book is to emphasize the formal statistical features of the practice of
equating, linking, and scaling. The book encourages the view and discusses the quality of …

The Kullback–Leibler divergence (KLD) is a widely used method for measuring the fit of two
distributions. In general, the distribution of the KLD is unknown. Under reasonable …

This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated
in the context of Latent Class Analysis (LCA). It is written for students in quantitative …

In this paper, we study the identification of a particular case of the 3PL model, namely when
the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G …

Probabilistic surveys on macroeconomic variables provide a wealth of information to the
applied researcher. Extracting and using this information is not a trivial task, however. This …

Parameters in time series and other dynamic models often show complex range restrictions
and their distributions may deviate substantially from multivariate normal or other standard …

Why identification is relevant for model construction? In 1922, RA Fisher pointed out that, in
spite of the large amount of fruitful applications of statistics, its basic principles were still in a …

We study the identification and consistency of Bayesian semiparametric IRT-type models,
where the uncertainty on the abilities' distribution is modeled using a prior distribution on the …

It is known that the Rasch model is a special two‐level hierarchical generalized linear model
(HGLM). This article demonstrates that the many‐faceted Rasch model (MFRM) is also a …