Addressing heterogeneous populations in latent variable settings through robust estimation.

ABSTRACT: Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability th

SUBMITTER: Nieser KJ 

PROVIDER: S-EPMC9035483 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature