Publications for Copula Dependence Modeling: Theory and Applications

• E. Cormier, C. Genest & J.G. Nešlehová (December 2014). Using B-splines for nonparametric inference on bivariate extreme-value copulas. Extremes, 17 (4), 633-659. DOI: 10.1007/s10687-014-0199-4.
• M.-P. Côté & C. Genest (March 2015). A copula-based risk aggregation model. The Canadian Journal of Statistics, 43 (1), in press. DOI: 10.1002/cjs.11238.
• F. Tounkara & L.-P. Rivest (2015). Mixture regression models for closed population capture-recapture data. Biometrics, 71, 721-730.
• J.-F. Quessy & O. Kortbi (2016). Minimum-distance statistics for the selection of an asymmetric copula in Khoudraji’s class of models. Statistica Sinica, 16, 177-204.
• C. Genest & F. Chebana (2016). Copula modeling in hydrologic frequency analysis. Chapter 30 of Handbook of Applied Hydrology (V.P. Singh, Editor). McGraw-Hill, New York, 10 pp.
• J.-F. Quessy (2016). A general framework for testing homogeneity hypotheses about copulas extracted from a multivariate vector. Electronic Journal of Statistics, 10, 1064-1097.
• H. Joe & P. Sang (2016). Multivariate models for dependent clusters of variables with conditional independence given aggregation variables. Computational Statistics and Data Analysis, 97, 114-132.
• L.-P. Rivest, F. Verret & S. Baillargeon (2016). Unit level small area estimation with copulas. Canadian Journal of Statistics, 44, 397-415.
• J. Garrido, C. Genest & J. Schulz (2016). Generalized linear models for dependent claims frequency and severity. Insurance: Mathematics & Economics, 70, 2015-215.
• M.-P. Côté, C. Genest & A. Abdallah (2016). Rank-based methods for modeling dependence between loss triangles. European Actuarial Journal, 6, 377-408.
• S.A. Aissaoui, C. Genest & M. Mesfioui (2017). A second look at inference for bivariate Skellam distributions. Stat, 6, in press.
• L. Hua & H. Joe (2017). Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis.
• H. Joe (2017). Dependence properties of conditional distributions of some copula models. Methodology and Computing in Applied Probability, accepted January 2017. For special issue in the memory of Moshe Shaked.
• J.-F. Quessy, L.-P. Rivest & M.-H. Toupin (2016). On the chi-square copula. Journal of Multivariate Analysis, 150, 40-60.

Contributions by other members of the team:

• F. Camirand-Lemyre, T. Bouezmarni & J.-F. Quessy. Estimation of conditional copulas: Revisiting asymptotic results in the i.i.d. case, and extension to serial dependence. Journal of Business and Economic Statistics (submitted).
• L.-P. Rivest, F. Verret & S. Baillargeon (December 2015). Estimation of the parameters in copula models for small areas. SSC Annual Meeting, June 2015. Proceedings of the Survey Methods Section.
• E. Levi & R. Craiu (December 2015). Gaussian process single index models for conditional copulas.
• C. Hasler, R. Craiu & L.-P. Rivest (August 2016). Vine copula models in multiple imputation methods. International Statistical.
• F. Camirand-Lemyre (2016). Multiplier bootstrap methods for conditional distributions. Statistics and Computing.
• I. Kojadinovic, J.-F. Quessy & T. Rohmer (2016). Testing the constancy of Spearman’s rho in multivariate time series.
• E.F. Acar (December 2015). Non-simplified vine copulas.
• C. Geerdens, E.F. Acar & P. Janssen (December 2015). Conditional copula models for right-censored clustered event time data. Biometrics.
• K. Zhao & E.F. Acar (December 2015). Conditional dependence models under covariate measurement error.
• E.F. Acar & C. Czado (2017). Dynamic Vine Copulas for Multivariate Time Series  (invited for submission to the Special Issue “Recent developments in copula models” in Econometrics).
• E. Hoque, P. Azimaee & E. F. Acar (2017). Prediction-based model selection for vine copula models.
• A. Nessie & E. F. Acar (2017). Factor copula analysis for multivariate ordinal data: revisiting the polychoric correlation.