The predictive accuracy of feed forward neural networks and multiple regression in the case of heteroscedastic data

This paper compares the performances of neural networks and regression analysis when the data deviate from the homoscedasticity assumption of regression. To carry out this comparison, datasets are simulated that vary systematically on various dimensions like sample size, noise levels and number of independent variables. Analysis is performed using appropriate experimental designs and the results are presented. Prediction intervals for both the methods for the case of nonconstant error variance are also calculated and are graphically compared. Two real life data sets that are heteroscedastic have been analyzed and the findings are in line with the results obtained from experiments using simulated data sets.