Quantile Regression (eBook)
John Wiley & Sons (Verlag)
978-1-118-86364-0 (ISBN)
Contains an overview of several technical topics of Quantile Regression
Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background.
The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text:
- Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators
- Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles
- Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem
- Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression
- Offers an analysis that is both theoretically and practical
- Presents real data examples and graphical representations to explain the technical issues
Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.
Marilena Furno, Department of Agriculture, University of Naples Federico II, Italy
Domenico Vistocco, Department of Economics and Law, University of Cassino, Italy
Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background. The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text: Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression Offers an analysis that is both theoretically and practical Presents real data examples and graphical representations to explain the technical issues Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.
Marilena Furno, Department of Agriculture, University of Naples Federico II, Italy Domenico Vistocco, Department of Economics and Law, University of Cassino, Italy
| Erscheint lt. Verlag | 18.7.2018 |
|---|---|
| Reihe/Serie | Wiley Series in Probability and Statistics | Wiley Series in Probability and Statistics |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | Angewandte Wahrscheinlichkeitsrechnung u. Statistik • Applied Probability & Statistics • autoregressive models • Barrodale-Roberts algorithm for median and quantile regression • Bootstrap • Cointegration • Conditionally heteroskedastic models</p> • Contaminated errors • Correlation • dual plot • Elemental sets • Expectiles • Extremal quantiles • Finance & Investments • Financial Engineering • Finanztechnik • Finanz- u. Anlagewesen • Geometrical interpretation of the quantile regression problem • Inference in the unit root model • Influence function and diagnostic tools • Linear Programming • Linear programming formulation of the quantile regression problem • <p>Robust regression • m-estimators • M-quantiles • non-stationarity • Quantile regression process • Regression Analysis • Regressionsanalyse • Resampling and subsampling • Revised simplex algorithm • Simplex Algorithm • Spurious regression • Statistics • Statistik • Tests of changing coefficients • Treatment effect and decomposition |
| ISBN-10 | 1-118-86364-X / 111886364X |
| ISBN-13 | 978-1-118-86364-0 / 9781118863640 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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