This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers. The following are a few questions that are considered in this book and are explored: how robust is the model to outliers, could the model be made more robust, which points will have a high leverage, what are good starting values for the fitting algorithm, etc. Discussions include the use of MLP models with spatial data as well as the influence and sensitivity curves of the MLP. The question of why the MLP is a (fairly) robust model is answered and modifications to make it very robust are considered.
A Statistical Approach to Neural Networks for Pattern Recognition