Estimation of an errors-in-variables model with replication under heavy-tailed distributions

摘要

The measurement error model is frequently used in various scientific fields, such as engineering, medicine, chemistry, etc. In this work, we consider a new replicated structural measurement error model in which the random errors and the unobserved covariates jointly follow scale mixtures of normal (SMN) distributions. Maximum likelihood estimates are computed via the EM type algorithm method. The SMN measurement error model provides an appealing robust alternative to the usual model based on normal distributions.