Predictive Modelling: An Assessment Through Validation Techniques

Abstract

In this investigation, various statistical models were fitted on simulated symmetric and asymmetric data. Fitting of models was carried out with the help of various libraries in R studio, and various selection criteria were also used while fitting of models. In order to evaluate different validation techniques the simulated data was divided in training and testing data set and various functions in R were developed for the purpose of validation. Coefficient summary revealed that all statistical models were statistically significant across both symmetric as well as asymmetric distributions. In preliminary analysis TFEM (Type First Exponential Model) was found out to be the best linear model across both symmetric and asymmetric distributions with lower values of RMSE, MAE, BIAS, AIC and BIC. Among non-linear models, Haung model was found out to be best model across both the distributions as it has lower values of RMSE, MAE etc. Different validation techniques were used in the present study. Lower rates of prediction error in comparison to its counter parts, 5-folded cross validation performed better across all the statistical models.