Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and...

Highly non-elliptical posterior distributions may occur in several econometric models, in particular, when the likelihood information is allowed to dominate and data information is weak. We explain the issue of highly non-elliptical posteriors in a model for the effect of education on income...

Abstract This paper deals with statistical tests on the components of mixture densities. We propose to test whether the densities of two independent samples of independent random variables Y 1 ,..., Y n and Z 1 ,..., Z n result from the same mixture of M components or not. We provide a test...

A common feature of ecological datasets is their tendency to contain many zero values. Statistical inference based on such data is likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for...

The U.S. National Airspace System (NAS) is inherently highly stochastic. Yet,many existing decision support tools for air traffic flow management take a deterministic approach to problem solving. In this study, we focus on the flight departure delays because such delays serve as inputs to many...

This paper reviews recent developments in nonparametric identi.cation of mea- surement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to...

Understanding who in the population is psychologically resilient in the face of major life events, and who is not, is important for policies that target reductions in disadvantage. In this paper we construct a measure of adult resilience, document its distribution, and test its predictability by...

In experiments of decision-making under risk, structural mixture models allow us to take a menu of theories about decision-making to the data, estimating the fraction of people who behave according to each model. While studies using mixture models typically focus only on how prevalent each of...

Efron's elegant approach to g-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer-Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and...