In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By...

A d-dimensional nonparametric additive regression model with dependent observations is considered. Using the marginal integration technique and wavelets methodology, we develop a new adaptive estimator for a component of the additive regression function. Its asymptotic properties are...

<Para ID="Par1">In this work the precision of point and interval estimators for some performance measures for risky financial assets is analyzed and the conditions under which the point estimators are asymptotically normally distributed are provided. The findings of this research suggest that a huge number of...</para>