Events

Date: 28 May 2026   Time: 14:00 - 15:00     

Location: Hybrid: MB503, SMS, QMUL, or via the Teams link below

Latent variable models are widely used in the social, behavioural, and health sciences to learn the latent structure underlying multivariate data. These models typically assume that the relationship between the set of latent variables and observed variables is identical for all measurement units. In practice, subpopulations may exist where the conditional distribution of a subset of observed variables given the latent variables differs systematically. Detecting such heterogeneity is challenging when both the subpopulations and the affected variables are unknown a priori. To address this problem, this talk presents a hybrid model that probabilistically assigns observations to discrete latent classes, where within each class, a continuous latent variable with class-specific distribution governs the observed variables. We propose a regularised marginal likelihood estimator that enables simultaneous identification of latent classes and selection of heterogeneous variables. The approach is illustrated using data from both a personality assessment and a large-scale educational test, where we apply the proposed method to identify groups that differ on specific variables beyond what is explained by the latent variable. Such patterns have important implications for the validity of these instruments.