On Structure, Family and Parameter Estimation of Hierarchical Archimedean Copulas
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478633" target="_blank" >RIV/67985807:_____/17:00478633 - isvavai.cz</a>
Alternative codes found
RIV/47813059:19520/17:00010847
Result on the web
<a href="http://dx.doi.org/10.1080/00949655.2017.1365148" target="_blank" >http://dx.doi.org/10.1080/00949655.2017.1365148</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/00949655.2017.1365148" target="_blank" >10.1080/00949655.2017.1365148</a>
Alternative languages
Result language
angličtina
Original language name
On Structure, Family and Parameter Estimation of Hierarchical Archimedean Copulas
Original language description
Research on structure determination and parameter estimation of hierarchical Archimedean copulas (HACs) has so far mostly focused on the case in which all appearing Archimedean copulas belong to the same Archimedean family. The present work addresses this issue and proposes a new approach for estimating HACs that involve different Archimedean families. It is based on employing goodness-of-fit test statistics directly into HAC estimation. The approach is summarized in a simple algorithm, its theoretical justification is given and its applicability is illustrated by several experiments, which include estimation of HACs involving up to five different Archimedean families.
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
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
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 Computation and Simulation
ISSN
0094-9655
e-ISSN
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Volume of the periodical
87
Issue of the periodical within the volume
17
Country of publishing house
GB - UNITED KINGDOM
Number of pages
64
Pages from-to
3261-3324
UT code for WoS article
000417048000002
EID of the result in the Scopus database
2-s2.0-85028562700