A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring

Articles in the same Issue

1. Cost-efficiency in multivariate Lévy models
2. Building bridges between Mathematics, Insurance and Finance
3. On the construction of low-parametric families of min-stable multivariate exponential distributions in large dimensions
4. Equivalent or absolutely continuous probability measures with given marginals
5. Forecasting time series with multivariate copulas
6. Measuring association via lack of co-monotonicity: the LOC index and a problem of educational assessment
7. Dependence Measuring from Conditional Variances
8. An analysis of the Rüschendorf transform - with a view towards Sklar’s Theorem
9. Seven Proofs for the Subadditivity of Expected Shortfall
10. High level quantile approximations of sums of risks
11. Multivariate Markov Families of Copulas
12. Quantile of a Mixture with Application to Model Risk Assessment
13. A Journey from Statistics and Probability to Risk Theory An interview with Ludger Rüschendorf
14. Bivariate copulas, norms and non-exchangeability
15. On the tail dependence in bivariate hydrological frequency analysis
16. A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring
17. A classification method for binary predictors combining similarity measures and mixture models