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.
Quantile Regression : theory and Applications / Cristina Davino, Marilena Furno, Domenico Vistocco
| Quantile Regression : theory and Applications / Cristina Davino, Marilena Furno, Domenico Vistocco |
| Autore | Davino, Cristina |
| Pubbl/distr/stampa | Chichester : Wiley, 2014 |
| Descrizione fisica | XIV, 260 p. : ill. ; 24 cm |
| Disciplina | 519.5 |
| Altri autori (Persone) |
Furno, Marilena <1957- >
Vistocco, Domenico |
| Collana | Wiley series in probability and statistics |
| Soggetto non controllato | Analisi di regressione |
| ISBN | 978-1-119-97528-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-990009847860403321 |
Davino, Cristina
|
||
| Chichester : Wiley, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco
| Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco |
| Autore | Davino Cristina |
| Pubbl/distr/stampa | Chichester, England : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (290 p.) |
| Disciplina | 519.5/36 |
| Altri autori (Persone) |
FurnoMarilena <1957->
VistoccoDomenico |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Quantile regression
Regression analysis |
| ISBN |
1-118-75271-6
1-118-75268-6 1-118-75319-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Quantile Regression: Theory and Applications; Copyright; Contents; A.2.2 Summary statistics; Preface; Acknowledgments; Introduction; Nomenclature; 1 A visual introduction to quantile regression; Introduction; 1.1 The essential toolkit; 1.1.1 Unconditional mean, unconditional quantiles and surroundings; 1.1.2 Technical insight: Quantiles as solutions of a minimizationproblem; 1.1.3 Conditional mean, conditional quantiles and surroundings; 1.2 The simplest QR model: The case of the dummy regressor; 1.3 A slightly more complex QR model: The case of a nominal regressor
1.4 A typical QR model: The case of a quantitative regressor1.5 Summary of key points; References; 2 Quantile regression: Understanding how and why; Introduction; 2.1 How and why quantile regression works; 2.1.1 The general linear programming problem; 2.1.2 The linear programming formulation for the QR problem; 2.1.3 Methods for solving the linear programming problem; 2.2 A set of illustrative artificial data; 2.2.1 Homogeneous error models; 2.2.2 Heterogeneous error models; 2.2.3 Dependent data error models; 2.3 How and why to work with QR; 2.3.1 QR for homogeneous and heterogeneous models 2.3.2 QR prediction intervals2.3.3 A note on the quantile process; 2.4 Summary of key points; References; 3 Estimated coefficients and inference; Introduction; 3.1 Empirical distribution of the quantile regression estimator; 3.1.1 The case of i.i.d. errors; 3.1.2 The case of i.ni.d. errors; 3.1.3 The case of dependent errors; 3.2 Inference in QR, the i.i.d. case; 3.3 Wald, Lagrange multiplier, and likelihood ratio tests; 3.4 Summary of key points; References; 4 Additional tools for the interpretation and evaluation of thequantile regression model; Introduction; 4.1 Data pre-processing 4.1.1 Explanatory variable transformations4.1.2 Dependent variable transformations; 4.2 Response conditional density estimations; 4.2.1 The case of different scenario simulations; 4.2.2 The case of the response variable reconstruction; 4.3 Validation of the model; 4.3.1 Goodness of fit; 4.3.2 Resampling methods; 4.4 Summary of key points; References; 5 Models with dependent and with non-identically distributed data; Introduction; 5.1 A closer look at the scale parameter, the independent andidentically distributed case; 5.1.1 Estimating the variance of quantile regressions 5.1.2 Confidence intervals and hypothesis testing on theestimated coefficients5.1.3 Example for the i.i.d. case; 5.2 The non-identically distributed case; 5.2.1 Example for the non-identically distributed case; 5.2.2 Quick ways to test equality of coefficients across quantilesin Stata; 5.2.3 The wage equation revisited; 5.3 The dependent data model; 5.3.1 Example with dependent data; 5.4 Summary of key points; References; Appendix 5.A Heteroskedasticity tests and weighted quantileregression, Stata and R codes 5.A.1 Koenker and Basset test for heteroskedasticity comparingtwo quantile regressions |
| Record Nr. | UNINA-9910138993403321 |
|
Davino Cristina
|
||
| Chichester, England : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantile regression : theory and applications / Cristina Davino, Marilena Furno, Domenico Vistocco
| Quantile regression : theory and applications / Cristina Davino, Marilena Furno, Domenico Vistocco |
| Autore | Davino, Cristina |
| Pubbl/distr/stampa | Chichester, : Wiley, 2014 |
| Descrizione fisica | XVI, 260 p. : ill. : 24 cm. |
| Disciplina | 519.536 |
| Altri autori (Persone) |
Furno, Marilena
Vistocco, Domenico |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Regressione |
| ISBN | 9781119975281 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAS-UBO4052939 |
|
Davino, Cristina
|
||
| Chichester, : Wiley, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Cassino | ||
| ||
Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco
| Quantile regression : theory and applications / / Cristina Davino, Marilena Furno, Domenico Vistocco |
| Autore | Davino Cristina |
| Pubbl/distr/stampa | Chichester, England : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (290 p.) |
| Disciplina | 519.5/36 |
| Altri autori (Persone) |
FurnoMarilena <1957->
VistoccoDomenico |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Quantile regression
Regression analysis |
| ISBN |
1-118-75271-6
1-118-75268-6 1-118-75319-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Quantile Regression: Theory and Applications; Copyright; Contents; A.2.2 Summary statistics; Preface; Acknowledgments; Introduction; Nomenclature; 1 A visual introduction to quantile regression; Introduction; 1.1 The essential toolkit; 1.1.1 Unconditional mean, unconditional quantiles and surroundings; 1.1.2 Technical insight: Quantiles as solutions of a minimizationproblem; 1.1.3 Conditional mean, conditional quantiles and surroundings; 1.2 The simplest QR model: The case of the dummy regressor; 1.3 A slightly more complex QR model: The case of a nominal regressor
1.4 A typical QR model: The case of a quantitative regressor1.5 Summary of key points; References; 2 Quantile regression: Understanding how and why; Introduction; 2.1 How and why quantile regression works; 2.1.1 The general linear programming problem; 2.1.2 The linear programming formulation for the QR problem; 2.1.3 Methods for solving the linear programming problem; 2.2 A set of illustrative artificial data; 2.2.1 Homogeneous error models; 2.2.2 Heterogeneous error models; 2.2.3 Dependent data error models; 2.3 How and why to work with QR; 2.3.1 QR for homogeneous and heterogeneous models 2.3.2 QR prediction intervals2.3.3 A note on the quantile process; 2.4 Summary of key points; References; 3 Estimated coefficients and inference; Introduction; 3.1 Empirical distribution of the quantile regression estimator; 3.1.1 The case of i.i.d. errors; 3.1.2 The case of i.ni.d. errors; 3.1.3 The case of dependent errors; 3.2 Inference in QR, the i.i.d. case; 3.3 Wald, Lagrange multiplier, and likelihood ratio tests; 3.4 Summary of key points; References; 4 Additional tools for the interpretation and evaluation of thequantile regression model; Introduction; 4.1 Data pre-processing 4.1.1 Explanatory variable transformations4.1.2 Dependent variable transformations; 4.2 Response conditional density estimations; 4.2.1 The case of different scenario simulations; 4.2.2 The case of the response variable reconstruction; 4.3 Validation of the model; 4.3.1 Goodness of fit; 4.3.2 Resampling methods; 4.4 Summary of key points; References; 5 Models with dependent and with non-identically distributed data; Introduction; 5.1 A closer look at the scale parameter, the independent andidentically distributed case; 5.1.1 Estimating the variance of quantile regressions 5.1.2 Confidence intervals and hypothesis testing on theestimated coefficients5.1.3 Example for the i.i.d. case; 5.2 The non-identically distributed case; 5.2.1 Example for the non-identically distributed case; 5.2.2 Quick ways to test equality of coefficients across quantilesin Stata; 5.2.3 The wage equation revisited; 5.3 The dependent data model; 5.3.1 Example with dependent data; 5.4 Summary of key points; References; Appendix 5.A Heteroskedasticity tests and weighted quantileregression, Stata and R codes 5.A.1 Koenker and Basset test for heteroskedasticity comparingtwo quantile regressions |
| Record Nr. | UNINA-9910807951903321 |
|
Davino Cristina
|
||
| Chichester, England : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||