A new sequential algorithm for regression problems by using mixture distribution

Abstract

A newsequen tial method for the regression problems is studied. The suggested method is motivated by boosting methods in the classification problems. Boosting algorithms use the weighted data to update the estimator. In this paper we construct a sequential estimation method from the viewpoint of nonparametric estimation by using mixture distribution. The algorithm uses the weighted residuals of training data. We compare the suggested algorithm to the greedy algorithm by the simple simulation. © Springer-Verlag Berlin Heidelberg 2002.