Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness in comparison with Regression Analysis and the Neural Network methods. Comparing the estimated resul...

The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modiication aaects the construction, pruning and interpretation of a regression tree. The modiication is tested on artiicial and real-life domains. The results show that the mod-iication is beneecial as it leads to smaller classiica-tion errors of induced regression t...

A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N. Comparing the predicted residual errors with those derived from a linear model assumption provides...

This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates. For each predictor, MAR selects between linear or additive as determined the dataset. The proposed is an adaptation of Least Angle (LARS) and retains its efficiency in building sparse models. Constrained penaliz...