The Vine Philosopher

An interview with Roger Cooke

Fabrizio Durante 1 , Giovanni Puccetti 2 , Matthias Scherer 3 ,  and Steven Vanduffel 4
  • 1 Dipartimento di Scienze dell’Economia, Università del Salento, , Lecce, Italy
  • 2 Dipartimento di Economia, Management e Metodi Quantitativi, Università di Milano, , Milano, Italy
  • 3 Lehrstuhl für Finanzmathematik, Technische Universität München, , München, Germany
  • 4 Faculteit Economische en Sociale Wetenschappen, Vrije Universiteit , Brussel, Belgium

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  • [1] Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance Math. Econom. 44(2), 182-198.

  • [2] Bedford, T. and R. Cooke (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.

  • [3] Bedford, T. and R. M. Cooke (2002). Vines - a new graphical model for dependent random variables. Ann. Statist. 30(4), 1031-1068.

  • [4] Cooke, R., A. Golub, B. A. Wielicki, D. F. Young, M. G. Mlynczak, and R. R. Baize (2017). Using the social cost of carbon to value earth observing systems. Clim. Policy 17(3), 330-345.

  • [5] Cooke, R., H. Joe, and B. Chang (2015). Vine regression. RFF working paper. Available at

  • [6] Cooke, R., M. Keane, and W. Moran (1985). An elementary proof of Gleason’s theorem. Math. Proc. Cambridge Philos. Soc. 98(1), 117-128.

  • [7] Cooke, R., M. Mendel, and W. Thijs (1988). Calibration and information in expert resolution; a classical approach. Automatica24(1), 87-93.

  • [8] Cooke, R. M. (1991). Experts in Uncertainty. Oxford University Press, New York.

  • [9] Cooke, R. M. (1993). The total time on test statistic and age-dependent censoring. Statist. Probab. Lett. 18(4), 307-312.

  • [10] Cooke, R. M. (2015). Commentary: Messaging climate change uncertainty. Nat. Clim. Change 5(1), 8-10.

  • [11] Cooke, R. M., H. Joe, and K. Aas (2011). Vines arise. In D. Kurowicka and H. Joe (Eds.), Dependence Modeling. Vine Copula Handbook, pp. 37-71. World Sci. Publ., Hackensack NJ.

  • [12] Cooke, R. M., D. Kurowicka, and K. Wilson (2015). Sampling, conditionalizing, counting, merging, searching regular vines. J. Multivariate Anal. 138, 4-18.

  • [13] Cooke, R. M. and O. Morales-Napoles (2006). Competing risk and the Cox proportional hazard model. J. Statist. Plann. Inference 136(5), 1621-1637.

  • [14] Cox, D. R. (1972). Regression models and life-tables. J. Roy. Statist. Soc. Ser. B 34(2), 187-220.

  • [15] Feller, W. (1968). An Introduction to Probability Theory and its Applications. Vol. I and II. Third Edition. John Wiley & Sons, New York.

  • [16] Joe, H. (1994). Multivariate extreme-value distributions with applications to environmental data. Can. J. Stat. 22(1), 47-64.

  • [17] Joe, H. (2006). Generating random correlation matrices based on partial correlations. J. Multivariate Anal. 97(10), 2177-2189.

  • [18] Kurowicka, D. and R. M. Cooke (2006). Uncertainty Analysis with High Dimensional Dependence Modelling. John Wiley & Sons, Chichester.

  • [19] Kurowicka, D. and H. Joe (Eds.) (2011). Dependence Modeling. Vine Copula Handbook. World Sci. Publ., Hackensack NJ.

  • [20] Lewandowski, D., D. Kurowicka, and H. Joe (2009). Generating random correlation matrices based on vines and extended onion method. J. Multivariate Anal. 100(9), 1989-2001.

  • [21] Oppenheimer, M., C. M. Little, and R. M. Cooke (2016). Expert judgement and uncertainty quantification for climate change. Nat. Clim. Change 6(5), 445-451.

  • [22] Savage, L. J. (1954). The Foundations of Statistics. John Wiley & Sons, New York. Reprinted in [23].

  • [23] Savage, L. J. (1972). The Foundations of Statistics. Revised Edition. Dover Publications, New York.

  • [24] Wadge, G. and W. P. Aspinall (2014). A review of volcanic hazard and risk-assessment praxis at the Soufriere Hills Volcano, Montserrat from 1997 to 2011. In Wadge, G and Robertson, R.E.A. and Voight, B (Eds.), Eruption of Soufriere Hills Volcano, Montserrat from 2000 to 2010, pp. 439-456. Geological Society, London.

  • [25] Zadeh, L. A. (1965). Fuzzy sets. Inf. Control 8, 338-353.


Journal + Issues

Dependence Modeling aims to provide a medium for exchanging results and ideas in the area of multivariate dependence modeling. Topics include Copula methods, environmental sciences, estimation and goodness-of-fit tests, extreme-value theory, limit laws, mass transportations, measures of association, multivariate distributions and tests, quantitative risk management, risk assessment, risk models, risk measures and stochastic orders and time series.