Multivariate nonnegative trigonometric sums distributions for high-dimensional multivariate circular data

Juan José Fernández-Durán and María Mercedes Gregorio-Domínguez ()
Additional contact information
Juan José Fernández-Durán: ITAM
María Mercedes Gregorio-Domínguez: ITAM

Computational Statistics, 2025, vol. 40, issue 6, No 5, 2954 pages

Abstract: Abstract Fernández-Durán and Gregorio-Domínguez (2014) defined a family of probability distributions for a vector of circular random variables by considering multiple nonnegative trigonometric sums. These distributions are highly flexible and can present numerous modes and skewness. Several operations on these multivariate distributions were translated into operations on the vector of parameters; for instance, marginalization involves calculating the eigenvectors and eigenvalues of a matrix, and independence among subsets of the vector of circular variables translates to a Kronecker product of the corresponding subsets of the vector of parameters. Furthermore, it was demonstrated that the family of multivariate circular distributions based on nonnegative trigonometric sums is closed under marginalization and conditioning, that is, the marginal and conditional densities of any order are also members of the family. The derivation of marginal and conditional densities from the joint multivariate density is important when applying this model in practice to real datasets. A goodness-of-fit test based on the characteristic function and an alternative parameter estimation algorithm for high-dimensional circular data was presented and applied to a real dataset on the daily times of occurrence of maxima and minima of prices in financial markets.

Keywords: Vector of circular random variables; Independence; Marginalization; Conditional distribution; Kronecker product (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-024-01583-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:40:y:2025:i:6:d:10.1007_s00180-024-01583-1

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-024-01583-1

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().