Abstract
This paper considers the simplex regression model when there is measurement error in the covariate. We consider a structural approach where the measurement error follows a normal or gamma distribution. We apply a Monte Carlo EM algorithm to estimate the parameters using a pseudo-likelihood function. A simulation study is used to investigate the impact of ignoring the measurement error. Finally, the results are illustrated with a data set.
Acknowledgments
The authors are grateful to two anonymous referees and the Editor for very useful comments and suggestions. This work was partially supported by Conselho Nacional de Desenvolvimento Cientco e Tecnolgico (CNPq), Brazil, Programa de Apoio a Pesquisadores Emergentes - UFBA, Brazil and the Department of Statistical Sciences of the University of Toronto, Canada.