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 ...

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

Latent factors are variables that cannot be observed directly but can be inferred from a set of observable variables. For example, in psychology, bad conduct (latent factor) can be measured by how ...

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 ...

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 ...

Unfortunately, if you try to fit models of Form B or Form C without additional constraints, you cannot obtain unique estimates of the parameters. These models have four parameters (one coefficient and ...

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 ...