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.
Reinsurance : actuarial and statistical aspects / / Hansjörg Albrecher, University of Lausanne, Switzerland, Jan Beirlant, Katholieke Universiteit Leuven, BE, University of the Free State, South Africa, Jozef L. Teugels, Katholieke Universiteit Leuven, BE
| Reinsurance : actuarial and statistical aspects / / Hansjörg Albrecher, University of Lausanne, Switzerland, Jan Beirlant, Katholieke Universiteit Leuven, BE, University of the Free State, South Africa, Jozef L. Teugels, Katholieke Universiteit Leuven, BE |
| Autore | Albrecher Hansjörg |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (353 pages) : illustrations, tables |
| Disciplina | 368.0122 |
| Collana |
Wiley Series in Probability and Statistics
THEi Wiley ebooks |
| Soggetto topico |
Reinsurance
Actuarial science |
| ISBN |
1-119-41994-8
1-119-41993-X 1-119-41254-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910270936803321 |
Albrecher Hansjörg
|
||
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reinsurance : actuarial and statistical aspects / / Hansjörg Albrecher, University of Lausanne, Switzerland, Jan Beirlant, Katholieke Universiteit Leuven, BE, University of the Free State, South Africa, Jozef L. Teugels, Katholieke Universiteit Leuven, BE
| Reinsurance : actuarial and statistical aspects / / Hansjörg Albrecher, University of Lausanne, Switzerland, Jan Beirlant, Katholieke Universiteit Leuven, BE, University of the Free State, South Africa, Jozef L. Teugels, Katholieke Universiteit Leuven, BE |
| Autore | Albrecher Hansjörg |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (353 pages) : illustrations, tables |
| Disciplina | 368.0122 |
| Collana |
Wiley Series in Probability and Statistics
THEi Wiley ebooks |
| Soggetto topico |
Reinsurance
Actuarial science |
| ISBN |
1-119-41994-8
1-119-41993-X 1-119-41254-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910826887603321 |
|
Albrecher Hansjörg
|
||
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics of extremes [[electronic resource] ] : theory and applications / / Jan Beirlant ... [et al.], with contributions from Daniel De Waal, Chris Ferro
| Statistics of extremes [[electronic resource] ] : theory and applications / / Jan Beirlant ... [et al.], with contributions from Daniel De Waal, Chris Ferro |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2004 |
| Descrizione fisica | 1 online resource (514 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) | BeirlantJan |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Mathematical statistics
Maxima and minima |
| ISBN |
1-280-54155-5
9786610541553 0-470-01238-2 0-470-01237-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistics of Extremes; Contents; Preface; 1 WHY EXTREME VALUE THEORY?; 1.1 A Simple Extreme Value Problem; 1.2 Graphical Tools for Data Analysis; 1.2.1 Quantile-quantile plots; 1.2.2 Excess plots; 1.3 Domains of Applications; 1.3.1 Hydrology; 1.3.2 Environmental research and meteorology; 1.3.3 Insurance applications; 1.3.4 Finance applications; 1.3.5 Geology and seismic analysis; 1.3.6 Metallurgy; 1.3.7 Miscellaneous applications; 1.4 Conclusion; 2 THE PROBABILISTIC SIDE OF EXTREME VALUE THEORY; 2.1 The Possible Limits; 2.2 An Example; 2.3 The Fréchet-Pareto Case: g > 0
2.3.1 The domain of attraction condition2.3.2 Condition on the underlying distribution; 2.3.3 The historical approach; 2.3.4 Examples; 2.3.5 Fitting data from a Pareto-type distribution; 2.4 The (Extremal) Weibull Case: g < 0; 2.4.1 The domain of attraction condition; 2.4.2 Condition on the underlying distribution; 2.4.3 The historical approach; 2.4.4 Examples; 2.5 The Gumbel Case: g = 0; 2.5.1 The domain of attraction condition; 2.5.2 Condition on the underlying distribution; 2.5.3 The historical approach and examples; 2.6 Alternative Conditions for (C(g)) 2.7 Further on the Historical Approach2.8 Summary; 2.9 Background Information; 2.9.1 Inverse of a distribution; 2.9.2 Functions of regular variation; 2.9.3 Relation between F and U; 2.9.4 Proofs for section 2.6; 3 AWAY FROM THE MAXIMUM; 3.1 Introduction; 3.2 Order Statistics Close to the Maximum; 3.3 Second-order Theory; 3.3.1 Remainder in terms of U; 3.3.2 Examples; 3.3.3 Remainder in terms of F; 3.4 Mathematical Derivations; 3.4.1 Proof of (3.6); 3.4.2 Proof of (3.8); 3.4.3 Solution of (3.15); 3.4.4 Solution of (3.18); 4 TAIL ESTIMATION UNDER PARETO-TYPE MODELS; 4.1 A Naive Approach 4.2 The Hill Estimator4.2.1 Construction; 4.2.2 Properties; 4.3 Other Regression Estimators; 4.4 A Representation for Log-spacings and Asymptotic Results; 4.5 Reducing the Bias; 4.5.1 The quantile view; 4.5.2 The probability view; 4.6 Extreme Quantiles and Small Exceedance Probabilities; 4.6.1 First-order estimation of quantiles and return periods; 4.6.2 Second-order refinements; 4.7 Adaptive Selection of the Tail Sample Fraction; 5 TAIL ESTIMATION FOR ALL DOMAINS OF ATTRACTION; 5.1 The Method of Block Maxima; 5.1.1 The basic model; 5.1.2 Parameter estimation 5.1.3 Estimation of extreme quantiles5.1.4 Inference: confidence intervals; 5.2 Quantile View-Methods Based on (C(g)); 5.2.1 Pickands estimator; 5.2.2 The moment estimator; 5.2.3 Estimators based on the generalized quantile plot; 5.3 Tail Probability View-Peaks-Over-Threshold Method; 5.3.1 The basic model; 5.3.2 Parameter estimation; 5.4 Estimators Based on an Exponential Regression Model; 5.5 Extreme Tail Probability, Large Quantile and Endpoint Estimation Using Threshold Methods; 5.5.1 The quantile view; 5.5.2 The probability view; 5.5.3 Inference: confidence intervals 5.6 Asymptotic Results Under (C(g))-(C*(g)) |
| Record Nr. | UNINA-9910144723303321 |
| Hoboken, NJ, : Wiley, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics of extremes [[electronic resource] ] : theory and applications / / Jan Beirlant ... [et al.], with contributions from Daniel De Waal, Chris Ferro
| Statistics of extremes [[electronic resource] ] : theory and applications / / Jan Beirlant ... [et al.], with contributions from Daniel De Waal, Chris Ferro |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2004 |
| Descrizione fisica | 1 online resource (514 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) | BeirlantJan |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Mathematical statistics
Maxima and minima |
| ISBN |
1-280-54155-5
9786610541553 0-470-01238-2 0-470-01237-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistics of Extremes; Contents; Preface; 1 WHY EXTREME VALUE THEORY?; 1.1 A Simple Extreme Value Problem; 1.2 Graphical Tools for Data Analysis; 1.2.1 Quantile-quantile plots; 1.2.2 Excess plots; 1.3 Domains of Applications; 1.3.1 Hydrology; 1.3.2 Environmental research and meteorology; 1.3.3 Insurance applications; 1.3.4 Finance applications; 1.3.5 Geology and seismic analysis; 1.3.6 Metallurgy; 1.3.7 Miscellaneous applications; 1.4 Conclusion; 2 THE PROBABILISTIC SIDE OF EXTREME VALUE THEORY; 2.1 The Possible Limits; 2.2 An Example; 2.3 The Fréchet-Pareto Case: g > 0
2.3.1 The domain of attraction condition2.3.2 Condition on the underlying distribution; 2.3.3 The historical approach; 2.3.4 Examples; 2.3.5 Fitting data from a Pareto-type distribution; 2.4 The (Extremal) Weibull Case: g < 0; 2.4.1 The domain of attraction condition; 2.4.2 Condition on the underlying distribution; 2.4.3 The historical approach; 2.4.4 Examples; 2.5 The Gumbel Case: g = 0; 2.5.1 The domain of attraction condition; 2.5.2 Condition on the underlying distribution; 2.5.3 The historical approach and examples; 2.6 Alternative Conditions for (C(g)) 2.7 Further on the Historical Approach2.8 Summary; 2.9 Background Information; 2.9.1 Inverse of a distribution; 2.9.2 Functions of regular variation; 2.9.3 Relation between F and U; 2.9.4 Proofs for section 2.6; 3 AWAY FROM THE MAXIMUM; 3.1 Introduction; 3.2 Order Statistics Close to the Maximum; 3.3 Second-order Theory; 3.3.1 Remainder in terms of U; 3.3.2 Examples; 3.3.3 Remainder in terms of F; 3.4 Mathematical Derivations; 3.4.1 Proof of (3.6); 3.4.2 Proof of (3.8); 3.4.3 Solution of (3.15); 3.4.4 Solution of (3.18); 4 TAIL ESTIMATION UNDER PARETO-TYPE MODELS; 4.1 A Naive Approach 4.2 The Hill Estimator4.2.1 Construction; 4.2.2 Properties; 4.3 Other Regression Estimators; 4.4 A Representation for Log-spacings and Asymptotic Results; 4.5 Reducing the Bias; 4.5.1 The quantile view; 4.5.2 The probability view; 4.6 Extreme Quantiles and Small Exceedance Probabilities; 4.6.1 First-order estimation of quantiles and return periods; 4.6.2 Second-order refinements; 4.7 Adaptive Selection of the Tail Sample Fraction; 5 TAIL ESTIMATION FOR ALL DOMAINS OF ATTRACTION; 5.1 The Method of Block Maxima; 5.1.1 The basic model; 5.1.2 Parameter estimation 5.1.3 Estimation of extreme quantiles5.1.4 Inference: confidence intervals; 5.2 Quantile View-Methods Based on (C(g)); 5.2.1 Pickands estimator; 5.2.2 The moment estimator; 5.2.3 Estimators based on the generalized quantile plot; 5.3 Tail Probability View-Peaks-Over-Threshold Method; 5.3.1 The basic model; 5.3.2 Parameter estimation; 5.4 Estimators Based on an Exponential Regression Model; 5.5 Extreme Tail Probability, Large Quantile and Endpoint Estimation Using Threshold Methods; 5.5.1 The quantile view; 5.5.2 The probability view; 5.5.3 Inference: confidence intervals 5.6 Asymptotic Results Under (C(g))-(C*(g)) |
| Record Nr. | UNINA-9910830001503321 |
| Hoboken, NJ, : Wiley, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics of extremes : theory and applications / / Jan Beirlant ... [et al.], with contributions from Daniel De Waal, Chris Ferro
| Statistics of extremes : theory and applications / / Jan Beirlant ... [et al.], with contributions from Daniel De Waal, Chris Ferro |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2004 |
| Descrizione fisica | 1 online resource (514 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) | BeirlantJan |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Mathematical statistics
Maxima and minima |
| ISBN |
1-280-54155-5
9786610541553 0-470-01238-2 0-470-01237-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistics of Extremes; Contents; Preface; 1 WHY EXTREME VALUE THEORY?; 1.1 A Simple Extreme Value Problem; 1.2 Graphical Tools for Data Analysis; 1.2.1 Quantile-quantile plots; 1.2.2 Excess plots; 1.3 Domains of Applications; 1.3.1 Hydrology; 1.3.2 Environmental research and meteorology; 1.3.3 Insurance applications; 1.3.4 Finance applications; 1.3.5 Geology and seismic analysis; 1.3.6 Metallurgy; 1.3.7 Miscellaneous applications; 1.4 Conclusion; 2 THE PROBABILISTIC SIDE OF EXTREME VALUE THEORY; 2.1 The Possible Limits; 2.2 An Example; 2.3 The Fréchet-Pareto Case: g > 0
2.3.1 The domain of attraction condition2.3.2 Condition on the underlying distribution; 2.3.3 The historical approach; 2.3.4 Examples; 2.3.5 Fitting data from a Pareto-type distribution; 2.4 The (Extremal) Weibull Case: g < 0; 2.4.1 The domain of attraction condition; 2.4.2 Condition on the underlying distribution; 2.4.3 The historical approach; 2.4.4 Examples; 2.5 The Gumbel Case: g = 0; 2.5.1 The domain of attraction condition; 2.5.2 Condition on the underlying distribution; 2.5.3 The historical approach and examples; 2.6 Alternative Conditions for (C(g)) 2.7 Further on the Historical Approach2.8 Summary; 2.9 Background Information; 2.9.1 Inverse of a distribution; 2.9.2 Functions of regular variation; 2.9.3 Relation between F and U; 2.9.4 Proofs for section 2.6; 3 AWAY FROM THE MAXIMUM; 3.1 Introduction; 3.2 Order Statistics Close to the Maximum; 3.3 Second-order Theory; 3.3.1 Remainder in terms of U; 3.3.2 Examples; 3.3.3 Remainder in terms of F; 3.4 Mathematical Derivations; 3.4.1 Proof of (3.6); 3.4.2 Proof of (3.8); 3.4.3 Solution of (3.15); 3.4.4 Solution of (3.18); 4 TAIL ESTIMATION UNDER PARETO-TYPE MODELS; 4.1 A Naive Approach 4.2 The Hill Estimator4.2.1 Construction; 4.2.2 Properties; 4.3 Other Regression Estimators; 4.4 A Representation for Log-spacings and Asymptotic Results; 4.5 Reducing the Bias; 4.5.1 The quantile view; 4.5.2 The probability view; 4.6 Extreme Quantiles and Small Exceedance Probabilities; 4.6.1 First-order estimation of quantiles and return periods; 4.6.2 Second-order refinements; 4.7 Adaptive Selection of the Tail Sample Fraction; 5 TAIL ESTIMATION FOR ALL DOMAINS OF ATTRACTION; 5.1 The Method of Block Maxima; 5.1.1 The basic model; 5.1.2 Parameter estimation 5.1.3 Estimation of extreme quantiles5.1.4 Inference: confidence intervals; 5.2 Quantile View-Methods Based on (C(g)); 5.2.1 Pickands estimator; 5.2.2 The moment estimator; 5.2.3 Estimators based on the generalized quantile plot; 5.3 Tail Probability View-Peaks-Over-Threshold Method; 5.3.1 The basic model; 5.3.2 Parameter estimation; 5.4 Estimators Based on an Exponential Regression Model; 5.5 Extreme Tail Probability, Large Quantile and Endpoint Estimation Using Threshold Methods; 5.5.1 The quantile view; 5.5.2 The probability view; 5.5.3 Inference: confidence intervals 5.6 Asymptotic Results Under (C(g))-(C*(g)) |
| Record Nr. | UNINA-9910876872703321 |
| Hoboken, NJ, : Wiley, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||