19 June Goodness-of-fit for generalized linear latent variables models

Generalized Linear Latent Variables Models (GLLVM) enable the modelling of relationships between manifest and latent variables. These models are widely used in the social sciences. In a latent variable framework, one works with several unobservable quantities (latent scores, parameters) and it is herefore essential to choose a model as close as possible to the data. To test the appropriateness of...

19 June High Breakdown Inference in the Mixed Linear Model

Mixed linear models are used to analyze data in many settings. These models have a multivariate normal formulation in most cases. The maximum likelihood estimator (MLE) or the residual MLE (REML) is usually chosen to estimate the parameters. However, the latter are based on the strong assumption of exact multivariate normality. Welsh and Richardson have shown that these estimators are not robust...

19 June Modelling Lorenz Curves: robust and semi-parametric issues

Modelling Lorenz curves (LC) for stochastic dominance comparisons is central to the analysis of income distributions. It is conventional to use non-parametric statistics based on empirical income cumulants which are used in the construction of LC and other related second-order dominance criteria. However, although attractive because of its simplicity and its apparent flexibility, this approach...