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REMLA: Robust Expectation-Maximization Estimation for Latent Variable
Models
Traditional latent variable models assume that the population
is homogeneous, meaning that all individuals in the population are
assumed to have the same latent structure. However, this assumption is
often violated in practice given that individuals may differ in their
age, gender, socioeconomic status, and other factors that can affect
their latent structure. The robust expectation maximization (REM)
algorithm is a statistical method for estimating the parameters of a
latent variable model in the presence of population heterogeneity as recommended by
Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the
expectation-maximization (EM) algorithm, but it allows for the case
when all the data are generated by the assumed data generating model.
Documentation:
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