Possibility of Decrease in a Level of Data Correlation During Processing Small Samples Using Neural Networks by Generating New Statistic Tests

Statistic tests created in the 20th century may be mapped with some equivalent artificial neurons. As a result, a network of dozens of artificial neurons that combines dozens of known statistic tests may be used for the validation of the normality hypothesis. The quality of solutions made by a neura...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2020-05, Vol.1546 (1), p.12080
Main Authors: Ivanov, A I, Bannykh, A G, Lozhnikov, P S, Sulavko, A E, Inivatov, D P
Format: Article
Language:English
Subjects:
Data correlation
Hypotheses
Neural networks
Neurons
Normality
Statistical methods
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Statistic tests created in the 20th century may be mapped with some equivalent artificial neurons. As a result, a network of dozens of artificial neurons that combines dozens of known statistic tests may be used for the validation of the normality hypothesis. The quality of solutions made by a neural network depends on a number of used neurons (tests). This tendency gives rise to the task of creating new statistic tests (neurons) that first and foremost require low correlation of their decision with known tests. The paper presents a forecast of attainable confidential probabilities for the validation of the normality hypothesis for a small sample of 21 tests in a network consisting of 21 artificial neurons, where each one is mapped with one traditional statistic test. When new tests are used (that should be created in the 21st century), the correlation of data is expected to lower by far, which should allow an approximately 10-fold decrease in a number of error probabilities.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1546/1/012080