The aim of the paper is to develop hypothesis testing procedures both for variable selection and model adequacy to facilitate a model selection strategy for neural networks. The approach, based on statical inference tools, uses the subsampling to overcome the analytical and probabilistic difficulties related to the estimation of the sampling distribution of the test statistics involved. Some illustrative examples are also discussed. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
La Rocca, M., & Perna, C. (2005). Neural network modeling by subsampling. In Lecture Notes in Computer Science (Vol. 3512, pp. 200–207). Springer Verlag. https://doi.org/10.1007/11494669_25
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