Forecasting and Analysis of Marketing Data Using Neural Networks

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All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, methodology): “A similar “Perturb” VI measure has been used for neural networks, where noise is added to covariates (Recknagel et al., 1997; Yao et al., 1998; Scardi and Harding, 1999; Gevrey et al., 2003).”
Review and comparison of methods to study the contribution of variables in artificial neural network models (2003, methodology): “In fact, the mean square error (MSE) of the neural network output is expected to increase as a larger amount of noise is added to the selected input variable ( Yao et al., 1998; Scardi and Harding, 1999).”
All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, methodology): “A similar “Perturb” VI measure has been used for neural networks, where noise is added to covariates (Recknagel et al., 1997; Yao et al., 1998; Scardi and Harding, 1999; Gevrey et al., 2003).”
Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters (2003, methodology): “There are many ways to perform the sensitivity analysis (Helton, 1993; Zurada et al., 1994; Hamby, 1994; Yao et al., 1998).”