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Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula

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Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula. / Villa, Cristiano; Rubio Alvarez, Francisco Javier.

In: COMPUTATIONAL STATISTICS AND DATA ANALYSIS, Vol. 124, 08.2018, p. 197-219.

Research output: Contribution to journalArticle

Harvard

Villa, C & Rubio Alvarez, FJ 2018, 'Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula', COMPUTATIONAL STATISTICS AND DATA ANALYSIS, vol. 124, pp. 197-219. https://doi.org/10.1016/j.csda.2018.03.010

APA

Villa, C., & Rubio Alvarez, F. J. (2018). Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula. COMPUTATIONAL STATISTICS AND DATA ANALYSIS, 124, 197-219. https://doi.org/10.1016/j.csda.2018.03.010

Vancouver

Villa C, Rubio Alvarez FJ. Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula. COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 2018 Aug;124:197-219. https://doi.org/10.1016/j.csda.2018.03.010

Author

Villa, Cristiano ; Rubio Alvarez, Francisco Javier. / Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula. In: COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 2018 ; Vol. 124. pp. 197-219.

Bibtex Download

@article{28484db32fb94118b517bbecea39943d,
title = "Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula",
abstract = "An objective Bayesian approach to estimate the number of degrees of freedom for the multivariate distribution and for the -copula, when the parameter is considered discrete, is proposed. Inference on this parameter has been problematic for the multivariate and, for the absence of any method, for the -copula. An objective criterion based on loss functions which allows to overcome the issue of defining objective probabilities directly is employed. The support of the prior for is truncated, which derives from the property of both the multivariate and the -copula of convergence to normality for a sufficiently large number of degrees of freedom. The performance of the priors is tested on simulated scenarios 1and on real data: daily logarithmic returns of IBM and of the Center for Research in Security Prices Database.",
author = "Cristiano Villa and {Rubio Alvarez}, {Francisco Javier}",
year = "2018",
month = "8",
doi = "10.1016/j.csda.2018.03.010",
language = "English",
volume = "124",
pages = "197--219",
journal = "COMPUTATIONAL STATISTICS AND DATA ANALYSIS",
issn = "0167-9473",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Objective priors for the number of degrees of freedom of a multivariate distribution and the t-copula

AU - Villa, Cristiano

AU - Rubio Alvarez, Francisco Javier

PY - 2018/8

Y1 - 2018/8

N2 - An objective Bayesian approach to estimate the number of degrees of freedom for the multivariate distribution and for the -copula, when the parameter is considered discrete, is proposed. Inference on this parameter has been problematic for the multivariate and, for the absence of any method, for the -copula. An objective criterion based on loss functions which allows to overcome the issue of defining objective probabilities directly is employed. The support of the prior for is truncated, which derives from the property of both the multivariate and the -copula of convergence to normality for a sufficiently large number of degrees of freedom. The performance of the priors is tested on simulated scenarios 1and on real data: daily logarithmic returns of IBM and of the Center for Research in Security Prices Database.

AB - An objective Bayesian approach to estimate the number of degrees of freedom for the multivariate distribution and for the -copula, when the parameter is considered discrete, is proposed. Inference on this parameter has been problematic for the multivariate and, for the absence of any method, for the -copula. An objective criterion based on loss functions which allows to overcome the issue of defining objective probabilities directly is employed. The support of the prior for is truncated, which derives from the property of both the multivariate and the -copula of convergence to normality for a sufficiently large number of degrees of freedom. The performance of the priors is tested on simulated scenarios 1and on real data: daily logarithmic returns of IBM and of the Center for Research in Security Prices Database.

U2 - 10.1016/j.csda.2018.03.010

DO - 10.1016/j.csda.2018.03.010

M3 - Article

VL - 124

SP - 197

EP - 219

JO - COMPUTATIONAL STATISTICS AND DATA ANALYSIS

JF - COMPUTATIONAL STATISTICS AND DATA ANALYSIS

SN - 0167-9473

ER -

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