Keywords: latent variable, self consistency, stochastic approximation
Latent variable models are widely used, such as generalized linear mixed models and others. For such models, a general class of estimators can be defined by self-consistency, which includes the maximum likelihood estimator in the special case where a parametric likelihood function is available. We show that such estimators can be efficiently computed by stochastic approximation algorithms, along with the estimated variances.