Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Dependence modeling [[electronic resource] ] : vine copula handbook / / editors, Dorota Kurowicka, Harry Joe
| Dependence modeling [[electronic resource] ] : vine copula handbook / / editors, Dorota Kurowicka, Harry Joe |
| Pubbl/distr/stampa | Hackensack, N.J., : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (368 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KurowickaDorota
JoeHarry |
| Soggetto topico |
Copulas (Mathematical statistics)
Dependence (Statistics) Distribution (Probability theory) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-14441-7
9786613144416 981-4299-88-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Contents; 1. Introduction: Dependence Modeling D. Kurowicka; 2. Multivariate Copulae M. Fischer; 3. Vines Arise R. M. Cooke, H. Joe and K. Aas; 4. Sampling Count Variables with Specified Pearson Correlation: A Comparison between a Naive and a C-Vine Sampling Approach V. Erhardt and C. Czado; 5. Micro Correlations and Tail Dependence R. M. Cooke, C. Kousky and H. Joe; 6. The Copula Information Criterion and Its Implications for the Maximum Pseudo-Likelihood Estimator S. Grønneberg; 7. Dependence Comparisons of Vine Copulae with Four or More Variables H. Joe
8. Tail Dependence in Vine Copulae H. Joe9. Counting Vines O. Morales-Napoles; 10. Regular Vines: Generation Algorithm and Number of Equivalence Classes H. Joe, R. M. Cooke and D. Kurowicka; 11. Optimal Truncation of Vines D. Kurowicka; 12. Bayesian Inference for D-Vines: Estimation and Model Selection C. Czado and A. Min; 13. Analysis of Australian Electricity Loads Using Joint Bayesian Inference of D-Vines with Autoregressive Margins C. Czado, F. G ̈artner and A. Min; 14. Non-Parametric Bayesian Belief Nets versus Vines A. Hanea 15. Modeling Dependence between Financial Returns Using Pair-Copula Constructions K. Aas and D. Berg16. Dynamic D-Vine Model A. Heinen and A. Valdesogo; 17. Summary and Future Directions D. Kurowicka; Index |
| Record Nr. | UNINA-9910463930303321 |
| Hackensack, N.J., : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dependence modeling [[electronic resource] ] : vine copula handbook / / editors, Dorota Kurowicka, Harry Joe
| Dependence modeling [[electronic resource] ] : vine copula handbook / / editors, Dorota Kurowicka, Harry Joe |
| Pubbl/distr/stampa | Hackensack, N.J., : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (368 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KurowickaDorota
JoeHarry |
| Soggetto topico |
Copulas (Mathematical statistics)
Dependence (Statistics) Distribution (Probability theory) |
| ISBN |
1-283-14441-7
9786613144416 981-4299-88-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Contents; 1. Introduction: Dependence Modeling D. Kurowicka; 2. Multivariate Copulae M. Fischer; 3. Vines Arise R. M. Cooke, H. Joe and K. Aas; 4. Sampling Count Variables with Specified Pearson Correlation: A Comparison between a Naive and a C-Vine Sampling Approach V. Erhardt and C. Czado; 5. Micro Correlations and Tail Dependence R. M. Cooke, C. Kousky and H. Joe; 6. The Copula Information Criterion and Its Implications for the Maximum Pseudo-Likelihood Estimator S. Grønneberg; 7. Dependence Comparisons of Vine Copulae with Four or More Variables H. Joe
8. Tail Dependence in Vine Copulae H. Joe9. Counting Vines O. Morales-Napoles; 10. Regular Vines: Generation Algorithm and Number of Equivalence Classes H. Joe, R. M. Cooke and D. Kurowicka; 11. Optimal Truncation of Vines D. Kurowicka; 12. Bayesian Inference for D-Vines: Estimation and Model Selection C. Czado and A. Min; 13. Analysis of Australian Electricity Loads Using Joint Bayesian Inference of D-Vines with Autoregressive Margins C. Czado, F. G ̈artner and A. Min; 14. Non-Parametric Bayesian Belief Nets versus Vines A. Hanea 15. Modeling Dependence between Financial Returns Using Pair-Copula Constructions K. Aas and D. Berg16. Dynamic D-Vine Model A. Heinen and A. Valdesogo; 17. Summary and Future Directions D. Kurowicka; Index |
| Record Nr. | UNINA-9910788555203321 |
| Hackensack, N.J., : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||