A dimension reduction in neural network using copula matrix
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00561617" target="_blank" >RIV/67985807:_____/23:00561617 - isvavai.cz</a>
Alternative codes found
RIV/61988987:17610/23:A2402LOI
Result on the web
<a href="https://dx.doi.org/10.1080/03081079.2022.2108029" target="_blank" >https://dx.doi.org/10.1080/03081079.2022.2108029</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03081079.2022.2108029" target="_blank" >10.1080/03081079.2022.2108029</a>
Alternative languages
Result language
angličtina
Original language name
A dimension reduction in neural network using copula matrix
Original language description
In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of General Systems
ISSN
0308-1079
e-ISSN
1563-5104
Volume of the periodical
52
Issue of the periodical within the volume
2
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
131-146
UT code for WoS article
000846787700001
EID of the result in the Scopus database
2-s2.0-85136843132