Mixed effects models are frequently used for analyzing longitudinal data. Normality assumption of random effects distrbution is a routine assumption for these models, violation of which leads to model misspecification and misleading parameter estimates. We propose a semi-parametric approach using gradient function for random effect estimation. In the approach, we relax the normality assumption ...

This study extends the parametric estimation of the structural misclassification model (Arrieta, 2008) to analyze over-treatment of medical procedures to a semi-parametric estimation. The estimation is based on a doubleindex semi-parametric maximum likelihood with partial observability. The document shows that misspecification error due to an incorrect assumption about the error distribution ma...

• This review paper focuses on statistical issues arising in modeling univariate extremes of a random sample. In the last three decades there has been a shift from the area of parametric statistics of extremes, based on probabilistic asymptotic results in extreme value theory, towards a semi-parametric approach, where the estimation of the right and/or left tail-weight is performed under a quit...