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

A general two-level latent variable model is developed to provide a comprehensive framework for model comparison of various submodels. Nonlinear relationships among the latent variables in the ...

Suppose we observe samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and ...

In this paper, we propose a latent variable credit risk model for large loan port- folios. It employs the concept of nested Archimedean copulas to account for both a sector-type dependence structure ...

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 ...

In this paper, we develop a factor-type latent variable model for portfolio credit risk that accounts for stochastically dependent probability of default (PD), loss given default (LGD) and exposure at ...

Dynamical systems modeling is one of the most successfully implemented methodologies throughout mathematical oncology (1). Applications of these model first approaches have led to important insights ...