Testing for normality using neural networks

Show full item record

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

Files in this item

Files Size Format View

An open access version of this file is not available. Check "Publisher URL" field for access

This item appears in the following Collection(s)

Show full item record

Search RIT DML

Advanced Search