This paper focuses on semi-parametric estimation of multivariate expectiles for extreme levels risk. Multivariate and their extremes have been the focus plentiful research in recent years. In particular, it has noted that due to difficulty estimating these values elevated risk, an alternative formulation underlying optimization problem would be necessary. However, such a scenario, estimators on...

A semi parametric profil~ likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regime...

Outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric estimates. In this paper, we use Monte Carlo simulations demonstrate that methods, such as matching, are biased presence of outliers. Bad good leverage point outliers considered. Bias arises case bad points because they completely change distribution metrics used define counterfactuals; points, o...

This paper investigates the performance of three types of random coefficients logistic regression models; that is, models using parametric, semi-parametric, and nonparametric specifications of the distribution of the random effects. Whereas earlier studies focussed on models with a single random effect, here we look at models with multidimensional random effects (intercepts and slopes). Moreove...

We investigate the parameter estimation of regression models with fixed group effects, when variable is missing while group-related variables are available. This problem involves clustering to infer based on variables, and build a model target given eventually some additional variables. Thus, this can be formulated as joint distribution modeling The usual strategy for two-step approach starting...