Testing for normality using neural networks

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Title: Testing for normality using neural networks
Author: Wilson, Paul; Engel, Alejandro
Abstract: The most commonly used statistical procedures (t, F, chi-squared, ANOVA, regression) assume that samples have been taken at random from normal populations. In some cases the central limit theorem may provide a satisfactory approximation to normality, but, when samples are small, departures from normality can lead users of these procedures to false conclusions. In the paper on work-in-progress the authors describe the results of training an artificial neural network (ANN) to distinguish normal from non-normal samples for random samples of size 30. With little attempt at fine-tuning, the ANN achieves results comparable to those of the best known tests for normality.
Record URI: http://hdl.handle.net/1850/4690
Publishers URL: http://dx.doi.org/10.1109/ISUMA.1990.151340
Date: 1990-12-03

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