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Theoretical and Applied Aspects of Automating Multivariate Analysis Procedures

Theoretical and Applied Aspects of Automating Multivariate Analysis Procedures This paper addresses some important theoretical and applied aspects of modern computer science that are associated with analytical processing of scientific, technical, and economic information. The main trends in using automated non-parametric procedures for logical and mathematical processing of arrays (flows) of digital data are discussed. Some methodological aspects of developing new technological approaches and algorithms for analytical post-processing that allow one to design a wide range of multi-step procedures for assessment and multivariate analysis of scientific, technical, and economic data based on polygram estimation of functionals are considered. It is shown that the procedures and algorithms based on these methods of non-parametric statistics and multivariate data analysis can be useful in various applications, including the development of analytical technologies for big data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Documentation and Mathematical Linguistics Springer Journals

Theoretical and Applied Aspects of Automating Multivariate Analysis Procedures

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Allerton Press, Inc.
Subject
Computer Science; Information Storage and Retrieval
ISSN
0005-1055
eISSN
1934-8371
DOI
10.3103/S0005105518060043
Publisher site
See Article on Publisher Site

Abstract

This paper addresses some important theoretical and applied aspects of modern computer science that are associated with analytical processing of scientific, technical, and economic information. The main trends in using automated non-parametric procedures for logical and mathematical processing of arrays (flows) of digital data are discussed. Some methodological aspects of developing new technological approaches and algorithms for analytical post-processing that allow one to design a wide range of multi-step procedures for assessment and multivariate analysis of scientific, technical, and economic data based on polygram estimation of functionals are considered. It is shown that the procedures and algorithms based on these methods of non-parametric statistics and multivariate data analysis can be useful in various applications, including the development of analytical technologies for big data.

Journal

Automatic Documentation and Mathematical LinguisticsSpringer Journals

Published: Feb 6, 2019

References