GMDH utilizing BDS with insufficient modelling data

Abstract

It has been pointed out that the group method of data handling algorithm has a disadvantage that predicts an abnormal result in the event of shortage of modelling data. This paper presents a method of modelling that avoids the emergence of abnormal predicted values. For estimation of partial polynomial parameters, a bad data suppression estimator is employed. It is based on a non-quadratic cost function that reduces to the weighted least squares estimator in the absence of bad data. Real data are used to illustrate that this method prevents abnormal results.