Generalized Linear Latent Variable Models with Flexible Distribution of Latent Variables

Abstract. We consider a semi‐nonparametric specification for the density of latent variables in Generalized Linear Latent Variable Models (GLLVM). This specification is flexible enough to allow for an asymmetric, multi‐modal, heavy or light tailed smooth density. The degree of flexibility required by many applications of GLLVM can be achieved through this semi‐nonparametric specification with a finite number of parameters estimated by maximum likelihood. Even with this additional flexibility, we obtain an explicit expression of the likelihood for conditionally normal manifest variables. We show by simulations that the estimated density of latent variables capture the true one with good degree of accuracy and is easy to visualize. By analysing two real data sets we show that a flexible distribution of latent variables is a useful tool for exploring the adequacy of the GLLVM in practice.     

Ethnic Aspects of Gender Social Inequality

Egor Gaidar has just completed work on his book The State and Evolution. Analyzing the major trends in Russia's historical development, the author aims at a deeper understanding of what has happened to us in the recent past, which has been so stormy, of the precise point in our centuries-long journey at which we find ourselves, and of what is inscribed upon the milestone before which the wandering Russian knight stands once again, wondering: "Should I go left, or right?"