Maximum Likelihood Neural Network Prediction Models

David Faraggi, Richard Simon

Research output: Contribution to journalArticlepeer-review


Neural networks have received much attention in recent years mostly by non‐statisticians. The purpose of this paper is to incorporate neural networks in a non‐linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton‐Raphson algorithm. We use maximum likelihood estimators instead of the usual back‐propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non‐parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over‐fit the data, hence validation of results is crucial.

Original languageEnglish
Pages (from-to)713-725
Number of pages13
JournalBiometrical Journal
Issue number6
StatePublished - 1995
Externally publishedYes


  • Feed‐forward neural networks
  • Maximum likelihood
  • Non‐linear regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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