Quantile Regression: Estimation and Simulation, Volume 2 (Wiley Series in Probability and Statistics, Band 2) - Hardcover

Furno, Marilena; Vistocco, Domenico

Hardcover

ISBN 10: 1118863593 ISBN 13: 9781118863596

Verlag: Wiley, 2018

Inhaltsangabe

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.  

Über diesen Titel

Über die Autorin bzw. den Autor

Marilena Furno, Department of Agriculture, University of Naples Federico II, Italy

Domenico Vistocco, Department of Economics and Law, University of Cassino, Italy

Von der hinteren Coverseite

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 and treatments 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, and conditional heteroskedasticity via quantile regression
• Offers an analysis that is both theoretical 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.

Aus dem Klappentext

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 and treatments 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, and conditional heteroskedasticity via quantile regression
• Offers an analysis that is both theoretical 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.

Details zur Ausgabe

• Verlag: Wiley
• Erscheinungsdatum: 2018
• Sprache: Englisch
• ISBN 10: 1118863593
• ISBN 13: 9781118863596
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 307
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland