Statistical models and methods for dependence in insurance data
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Publication:458105
DOI10.1016/j.jkss.2011.03.005zbMath1296.62205OpenAlexW1984522550WikidataQ59278113 ScholiaQ59278113MaRDI QIDQ458105
Claudia Klüppelberg, Liang Peng, Stephan Haug
Publication date: 30 September 2014
Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)
Full work available at URL: http://mediatum.ub.tum.de/doc/1079270/document.pdf
copulahypothesis testingmultivariate statisticsinterval estimationpoint estimationdependence modelingrisk estimationtail dependence coefficientextreme dependenceextreme riskextreme value copulainference for copulasrisk modelingtail copula
Estimation in multivariate analysis (62H12) Applications of statistics to actuarial sciences and financial mathematics (62P05) Hypothesis testing in multivariate analysis (62H15) Measures of association (correlation, canonical correlation, etc.) (62H20)
Related Items
On some new dependence models derived from multivariate collective models in insurance applications, A note on tail dependence regression, Modeling of censored bivariate extremal events, On the worst and least possible asymptotic dependence, Copula density estimation by total variation penalized likelihood with linear equality constraints, Exact tail asymptotics in bivariate scale mixture models, Extremes and products of multivariate AC-product risks, Using B-splines for nonparametric inference on bivariate extreme-value copulas, Interval estimation for a measure of tail dependence, Jackknife empirical likelihood for parametric copulas, Baker- Lin-Huang Type Bivariate Distributions Based on Order Statistics
Uses Software
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