Simplex regression models with measurement error

Resumo: 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 an EM Monte Carlo 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.

Local: Auditório do IME para aqueles que desejam acompanhar de forma presencial e no link

https://www.youtube.com/channel/UCC96Rmc3qKEYkKk187IcLdA/live

Link da palestra:

https://www.youtube.com/watch?v=Kpkusm8pj5I

 

CV: http://lattes.cnpq.br/5279356698005104

Palestrante: 
Jalmar Carrasco
Universidade do Palestrante: 
DEST-UFBA
Data e Hora: 
Friday, 17 November, 2017 -
11:00 to 12:00