A Modern Statistical Methodology Neural networks are adaptive, computation-intensive techniques, loosely based on models of brain function. Other terms that have been applied to similar modeling endeavors are machine learning, genetic algorithms, and expert systems. The vast majority of the research papers and books on neural networks have appeared since 1990. Successful applications in such areas as marketing, pharmaceuticals, and government statistics have begun to appear even more recently.
Serious applications depend on the number-crunching power of modern computers.
Even though the field is relatively new, there are already many different kinds of applications. Using appropriate demographic data for cities, a market researcher may be able to identify cities that are similar to one in which a certain financial product has been successfully marketed. Recently, the Statistics Department at CSU Hayward contracted to offer a continuing education seminar on neural networks for statisticians from marketing and financial firms, government agencies, and other institutions where this methodology is increasingly used. An updated version of this list is given below. The Journal of Business Forecasting, Summer 1995, 17-20.