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We propose a method for obtaining maximum likelihood estimates in a modelwith continuous and binary outcomes. Combinations of left and right censoredobservations are also naturally modeled in this framework. The model andestimation procedure has been implemented in the R package lava.tobit.  The method is demonstrated on brain imaging and personality data wheremeasurement error on predictor variables is handled in a latent variableframework. A simulation study is conducted comparing the small sampleproperties of the MLE with a limited information estimator.

A latent variable model with mixed binary and continuous response variables