A general maximum likelihood analysis of measurement error in generalized linear models
This paper describes an EM algorithm for maximum likelihood estimation in generalized linear models (GLMs) with continuous measurement error in the explanatory variables. The algorithm is an adaptation of that for nonparametric maximum likelihood (NPML) estimation in overdispersed GLMs described in Aitkin (Statistics and Computing 6: 251-262, 1996). The measurement error distribution can be of any specified form, though the implementation described assumes normal measurement error. Neither the reliability nor the distribution of the true score of the variables with measurement error has to be known, nor are instrumental variables or replication required. Standard errors can be obtained by omitting individual variables from the model, as in Aitkin (1996). Several examples are given, of normal and Bernoulli response variables.
measurement error em algorithm nonparametric maximum likelihood random effects glm mixture model gaussian quadrature