AVOIDING BOUNDARY ESTIMATES IN LATENT CLASS ANALYSIS BY BAYESIAN POSTERIOR MODE ESTIMATION

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Latent Class Modeling with Covariates: Two Improved Three-Step Approaches (2010, methodology): “The explanation is that, as observed among others by Galindo-Garre and Vermunt (2006), ML estimation of LC models tends to yield 2Note that results are reported for three of the six covariate effect parameters; that is, the parameters for class 2.”
On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix (2011, methodology): “To address this problem, Maris (1999) discussed an approach based on maximum a posteriori estimation, also referred to as posterior mode estimation (PME; also see Fahrmeir & Tutz, 2001; Galindo-Garre & Vermunt, 2006; Gelman, Carlin, Stern, & Rubin, 1995; Schafer, 1997; Vermunt & Magidson, 2005).”
On the Estimation of Standard Errors in Cognitive Diagnosis Models (2018, background): “Posterior mode (PM) estimation has been suggested to overcome these problems (DeCarlo, 2011; Garre & Vermunt, 2006).”