Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation

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Multilevel modelling of complex survey data (2006, methodology): “In addition to conventional multilevel and latent variable models, this framework includes extensions such as multilevel structural equation models and models with nonparametric random effects or latent variable distributions (Rabe-Hesketh et al., 2003).”
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects (2005, background): “The program can also handle discrete random effects including nonparametric maximum likelihood (Heckman and Singer, 1984; Rabe-Hesketh et al., 2003) and is available from http://www.gllamm.org.”
Bayesian Multilevel Analysis and MCMC (2008, methodology): “Multilevel modeling entered a new phase in the 1980s, with the development of computer programs such as ML3, VARCL, and HLM using likelihood-based estimation approaches based on iterative generalized least squares [42], Fisher scoring [52], and the EM algorithm [e.”
Maximum Likelihood Estimation of Generalized Linear Models with Covariate Measurement Error (2003, methodology): “Table 1: Estimates of different models for diet and CHD data Classical Only NPMLE model indirect effect (dir.”