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Quantile regression : theory and applications

Author: Cristina Davino; Marilena Furno; Domenico Vistocco
Publisher: Chichester, West Sussex : Wiley, ©2014.
Series: Wiley series in probability and statistics.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
This unique reference offers a balance between methodology and application, illustrating how and why to use quantile regression in a variety of areas such as economics, finance and computing. It presents a complete treatment of quantile regression methods, including basic modeling, geometrical interpretation, estimation and inference issues, and diagnostic tools. Each method is explained and illustrated with  Read more...
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Additional Physical Format: Online version:
Davino, Cristina.
Quantile regression.
Chichester, West Sussex : John Wiley & Sons, Inc., [2013]
(DLC) 2013030463
Document Type: Book
All Authors / Contributors: Cristina Davino; Marilena Furno; Domenico Vistocco
ISBN: 9781119975281 111997528X 9781118863596 1118863593
OCLC Number: 842880964
Description: xvi, 260 pages : illustrations ; 24 cm.
Contents: Machine generated contents note: 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 minimization problem --
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 regressor --
1.5. 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 intervals --
2.3.3. 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 the quantile regression model --
Introduction --
4.1. Data pre-processing --
4.1.1. Explanatory variable transformations --
4.1.2. 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 and identically distributed case --
5.1.1. Estimating the variance of quantile regressions --
5.1.2. Confidence intervals and hypothesis testing on the estimated coefficients --
5.1.3. 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 quantiles in 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 quantile regression, Stata and R codes --
5.A.1. Koenker and Basset test for heteroskedasticity comparing two quantile regressions --
5.A.2. Koenker and Basset test for heteroskedasticity comparing all quantile regressions --
5.A.3. Quick tests for heteroskedasticity comparing quantile regressions --
5.A.4. Compute the individual role of each explanatory variable to the dependent variable. 5.A.5. R-codes for the Koenker and Basset test for heteroskedasticity --
Appendix 5.B Dependent data --
6. Additional models --
Introduction --
6.1. Nonparametric quantile regression --
6.1.1. Local polynomial regression --
6.1.2. Quantile smoothing splines --
6.2. Nonlinear quantile regression --
6.3. Censored quantile regression --
6.4. Quantile regression with longitudinal data --
6.5. Group effects through quantile regression --
6.6. Binary quantile regression --
6.7. Summary of key points --
References --
Appendix A Quantile regression and surroundings using R --
Introduction --
A.1. Loading data --
A.1.1. Text data --
A.1.2. Spreadsheet data --
A.1.3. Files from other statistical packages --
A.2. Exploring data --
A.2.1. Graphical tools --
A.2.2. Summary statistics --
A.3. Modeling data --
A.3.1. Ordinary least squares regression analysis --
A.3.2. Quantile regression analysis --
A.4. Exporting figures and tables --
A.4.1. Exporting figures --
A.4.2. Exporting tables --
References --
Appendix B Quantile regression and surroundings using SAS --
Introduction. B.1. Loading data --
B.1.1. Text data --
B.1.2. Spreadsheet data --
B.1.3. Files from other statistical packages --
B.2. Exploring data --
B.2.1. Graphical tools --
B.2.2. Summary statistics --
B.3. Modeling data --
B.3.1. Ordinary least squares regression analysis --
B.3.2. Quantile regression analysis --
B.4. Exporting figures and tables --
References --
Appendix C Quantile regression and surroundings using Stata --
Introduction --
C.1. Loading data --
C.1.1. Text data --
C.1.2. Spreadsheet data --
C.1.3. Files from other statistical packages --
C.2. Exploring data --
C.2.1. Graphical tools --
C.2.2. Summary statistics --
C.3. Modeling data --
C.3.1. Ordinary least squares regression analysis --
C.3.2. Quantile regression analysis --
C.4. Exporting figures and tables --
C.4.1. Exporting figures --
C.4.2. Exporting tables --
References.
Series Title: Wiley series in probability and statistics.
Responsibility: Cristina Davino, Marilena Furno, Domenico Vistocco.

Abstract:

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well  Read more...

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