Hierarchical Archimedean Copulas for Matlab and Octave: The HACopula Toolbox
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00525271" target="_blank" >RIV/67985807:_____/20:00525271 - isvavai.cz</a>
Alternative codes found
RIV/47813059:19520/20:A0000145
Result on the web
<a href="http://hdl.handle.net/11104/0309452" target="_blank" >http://hdl.handle.net/11104/0309452</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18637/jss.v093.i10" target="_blank" >10.18637/jss.v093.i10</a>
Alternative languages
Result language
angličtina
Original language name
Hierarchical Archimedean Copulas for Matlab and Octave: The HACopula Toolbox
Original language description
To extend the current implementation of copulas in MATLAB to non-elliptical distributions in arbitrary dimensions enabling for asymmetries in the tails, the toolbox HACopula provides functionality for modeling with hierarchical (or nested) Archimedean copulas. This includes their representation as MATLAB objects, evaluation, sampling, estimation and goodness-of-fit testing, as well as tools for their visual representation or computation of corresponding matrices of Kendall's tau and tail dependence coefficients. These are first presented in a quick-and-simple manner and then elaborated in more detail to show the full capability of HACopula. As an example, sampling, estimation and goodness-of-fit of a 100-dimensional hierarchical Archimedean copula is presented, including a speed up of its computationally most demanding part. The toolbox is also compatible with Octave, where no support for copulas in more than two dimensions is currently provided.
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
<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Statistical Software
ISSN
1548-7660
e-ISSN
—
Volume of the periodical
93
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
36
Pages from-to
1-36
UT code for WoS article
000542224700001
EID of the result in the Scopus database
2-s2.0-85085741796