Article
Open Access
Measuring association via lack of co-monotonicity: the LOC index and a problem of educational assessment
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Danang Teguh Qoyyimi
Published/Copyright:
June 18, 2015
Received: 2014-4-16
Accepted: 2015-6-6
Published Online: 2015-6-18
© 2015 Danang Teguh Qoyyimi and Ricardas Zitikis
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- Equivalent or absolutely continuous probability measures with given marginals
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- Measuring association via lack of co-monotonicity: the LOC index and a problem of educational assessment
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- An analysis of the Rüschendorf transform - with a view towards Sklar’s Theorem
- Seven Proofs for the Subadditivity of Expected Shortfall
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- A Journey from Statistics and Probability to Risk Theory An interview with Ludger Rüschendorf
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Keywords for this article
association;
co-monotonicity;
Liebscher coefficient;
LOC index;
education;
performance evaluation
Creative Commons
BY-NC-ND 3.0
Articles in the same Issue
- Cost-efficiency in multivariate Lévy models
- Building bridges between Mathematics, Insurance and Finance
- On the construction of low-parametric families of min-stable multivariate exponential distributions in large dimensions
- Equivalent or absolutely continuous probability measures with given marginals
- Forecasting time series with multivariate copulas
- Measuring association via lack of co-monotonicity: the LOC index and a problem of educational assessment
- Dependence Measuring from Conditional Variances
- An analysis of the Rüschendorf transform - with a view towards Sklar’s Theorem
- Seven Proofs for the Subadditivity of Expected Shortfall
- High level quantile approximations of sums of risks
- Multivariate Markov Families of Copulas
- Quantile of a Mixture with Application to Model Risk Assessment
- A Journey from Statistics and Probability to Risk Theory An interview with Ludger Rüschendorf
- Bivariate copulas, norms and non-exchangeability
- On the tail dependence in bivariate hydrological frequency analysis
- A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring
- A classification method for binary predictors combining similarity measures and mixture models