J
2023
Composite pseudo-likelihood estimation for pair-tractable copulas such as Archimedean, Archimax and related hierarchical extensions
GÓRECKI, Jan and Marius HOFERT
Basic information
Original name
Composite pseudo-likelihood estimation for pair-tractable copulas such as Archimedean, Archimax and related hierarchical extensions
Authors
GÓRECKI, Jan (203 Czech Republic, guarantor, belonging to the institution) and Marius HOFERT (276 Germany)
Edition
Journal of Statistical Computation and Simulation, Taylor & Francis, 2023, 0094-9655
Other information
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
Impact factor
Impact factor: 1.200 in 2022
RIV identification code
RIV/47813059:19520/23:A0000368
Organization unit
School of Business Administration in Karvina
Keywords in English
Maximum pseudo-likelihood estimator; aggregated maximum pseudo-likelihood estimator; bivariate margins; probability density function; Archimedean and Archimax copulas; hierarchical copulas
Links
GA21-03085S, research and development project.
V originále
The pairwise pseudo-likelihood estimator (PPLE) is introduced for estimating the parameters of pair-tractable copulas, so copulas with analytically or numerically tractable pairwise margins, such as Archimedean, hierarchical Archimedean, Archimax and hierarchical Archimax copulas. In cases where feasible, the PPLE is compared, by simulation, to the standard maximum pseudo-likelihood estimator (MPLE) in terms of bias, root mean squared error (RMSE) and run time. The PPLE is also compared to the aggregated MPLE (AMPLE) for hierarchical Archimedean copulas. The simulation results indicate that the PPLE has a bias and RMSE comparable to the MPLE for those Archimedean copulas where the latter is available. For hierarchical Archimedean and hierarchical Archimax copulas for which the MPLE is not easily available, the PPLE mostly outperforms the AMPLE in bias and RMSE, with a clear advantage in terms of run time.
Displayed: 17/2/2025 21:08