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01.11.2009 | Original Article

Nonlinear Poisson regression using neural networks: a simulation study

verfasst von: Nader Fallah, Hong Gu, Kazem Mohammad, Seyyed Ali Seyyedsalehi, Keramat Nourijelyani, Mohammad Reza Eshraghian

Erschienen in: Neural Computing and Applications | Ausgabe 8/2009

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Abstract

We describe a novel extension of the Poisson regression model to be based on a multi-layer perceptron, a type of neural network. This relaxes the assumptions of the traditional Poisson regression model, while including it as a special case. In this paper, we describe neural network regression models with six different schemes and compare their performances in three simulated data sets, namely one linear and two nonlinear cases. From the simulation study it is found that the Poisson regression models work well when the linearity assumption is correct, but the neural network models can largely improve the prediction in nonlinear situations.

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Titel: Nonlinear Poisson regression using neural networks: a simulation study
verfasst von: Nader Fallah
Hong Gu
Kazem Mohammad
Seyyed Ali Seyyedsalehi
Keramat Nourijelyani
Mohammad Reza Eshraghian
Publikationsdatum: 01.11.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 8/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0277-8

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