Inference for elliptical copula multivariate response regression models - Research Database, The University of York

By the same authors

Envelopes for censored quantile regression
Zhao, Y., Van Keilegom, I. & Shanshan, D., 24 Apr 2022, (Accepted/In press)
Article in Scandinavian Journal of Statistics
Parametric copula adjusted for non- and semi-parametric regression
Zhao, Y., Gijbels, I. & Van Keilegom, I., 7 Aug 2021, (Accepted/In press)
Article in Annals of Statistics
Inference for semiparametric Gaussian copula model adjusted for linear regression using residual ranks
Zhao, Y., Gijbels, I. & van Keilegom, I., 1 Nov 2020
Article in Bernoulli
Weak convergence of empirical copula processes indexed by functions
Radulović, D., Wegkamp, M. & Zhao, Y., 2017
Article in Bernoulli
Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas
Wegkamp, M. & Zhao, Y., May 2016
Article in Bernoulli

From the same journal

The Local Partial Autocorrelation Function and Some Applications
Killick, R., Knight, M. I., Nason, G. P. & Eckley, I. A., 10 Sep 2020
Article in Electronic Journal of Statistics
Restricted likelihood ratio testing in linear mixed models with general error covariance structure
Wiencierz, A., Greven, S. & Küchenhoff, H., 2011
Article in Electronic Journal of Statistics

From the same journal

Electronic Journal of Statistics (Journal)
Yue Zhao (Reviewer)
16 Jan 2021 → 23 Jan 2021
Activity: Publication peer-review and editorial work › Journal

Inference for elliptical copula multivariate response regression models

Research output: Contribution to journal › Article › peer-review

Links

https://projecteuclid.org/euclid.ejs/1553911236

Author(s)

Yue Zhao
Christian Genest

Department/unit(s)

Mathematics

Publication details

Journal	Electronic Journal of Statistics
Date	Published - 2019
Issue number	1
Volume	13
Original language	English

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations

York staff: edit these data