A neural network technique of generating empirical bivariate distribution functions

Abstract

The commonly used technique of generating empirical univariate cumulative distribution functions is extended to bivariate cases. It is implemented by using the monotonic back-propagation leastmean square neural network. An algorithm to generate empirical bivariate cumulative distribution functions using the neural network model is defined. Examples of generating simulated data using the suggested technique are demonstrated.