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Quantile Regression

Marilena Furno | Domenico Vistocco
€82.99
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Robust regression

A01=Domenico Vistocco
A01=Marilena Furno
Age Group_Uncategorized
Age Group_Uncategorized
Author_Domenico Vistocco
Author_Marilena Furno
automatic-update
Autoregressive models
Barrodale-Roberts algorithm for median and quantile regression
Bootstrap
Category1=Non-Fiction
Category=PB
Cointegration
Conditionally heteroskedastic models

Contaminated errors
COP=United States
Correlation
Delivery_Delivery within 10-20 working days
Dual plot
Elemental sets
eq_isMigrated=2
eq_nobargain
Expectiles
Extremal quantiles
Geometrical interpretation of the quantile regression problem
Inference in the unit root model
Influence function and diagnostic tools
Language_English
Linear programming
Linear programming formulation of the quantile regression problem
M-estimators
M-quantiles
Non-stationarity
PA=Available
Price_€50 to €100
PS=Active
Quantile regression process
Resampling and subsampling
Revised simplex algorithm
Simplex algorithm
SN=Wiley Series in Probability and Statistics
softlaunch
Spurious regression
Tests of changing coefficients
Treatment effect and decomposition

Product details

  • ISBN 9781118863596
  • Weight: 499g
  • Dimensions: 155 x 231mm
  • Publication Date: 14 Sep 2018
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description

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.  
About Domenico Vistocco | Marilena Furno

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

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

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