Latent variable modeling comprises a suite of methodologies that infer unobserved constructs from observable indicators, thereby enabling researchers to quantify abstract phenomena across diverse ...

Extending the traditional discrete choice model by incorporating latent psychological factors can help to better understand the individual's decision-making process and therefore to yield more ...

This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual ...

In this talk, I will discuss the development of interpretable machine learning models to test scientific hypotheses, with a specific focus on spinal motor control. Voluntary movement requires ...

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...