A study of methods of estimation using robust statistics

This paper looks for establishing how robust statistics theoretical models and intensive computation methods interact. The functional approach is discussed for establishing the convergence of estimators and predictors to the parameter, expressed as a functional of the unknown distribution. It allows the establishment of general conditions that sustain the use of Jack-Knife and Bootstrap for estimating confidence intervals, hypothesis testing etc., using only sampling information. Some bootstrap derived methods are remarked.