




RabeHesketh, Sophia; Skrondal, Anders; Pickles, Andrew. 
Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (RabeHesketh, Skrondal, and Pickles 2004). The program uses adaptive quadrature to evaluate the log likelihood, producing more reliable results than many other methods (RabeHesketh, Skrondal, and Pickles 2002). For a single covariate measured with error (assuming a classical measurement model), we describe a “wrapper” command, cme, that calls gllamm to estimate the model. The wrapper makes life easy for the user by accepting a simple syntax and data structure and producing extended and easily interpretable output. The commands for preparing the data and running gllamm can... 
Tipo: Journal Article 
Palavraschave: Covariate measurement error; Measurement model; Congeneric measurement model; Factor model; Adaptive quadrature; Nonparametric maximum likelihood; NPMLE; Latent class model; Empirical Bayes; Simulation; Wrapper; Sensitivity analysis; Gllamm; Cme; Research Methods/ Statistical Methods. 
Ano: 2003 
URL: http://purl.umn.edu/116185 
 

 


