4 edition of **Quantile Regression (Econometric Society Monographs)** found in the catalog.

- 178 Want to read
- 14 Currently reading

Published
**May 9, 2005**
by Cambridge University Press
.

Written in English

- Probability & statistics,
- Regression analysis,
- Econometrics,
- Business & Economics,
- Business / Economics / Finance,
- Business/Economics,
- Probability & Statistics - General,
- Business & Economics / Econometrics,
- Mathematical statistics

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 366 |

ID Numbers | |

Open Library | OL7766398M |

ISBN 10 | 0521845734 |

ISBN 10 | 9780521845731 |

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 as the software tools to implement the methods. Quantile regression. [Roger Koenker] -- "This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of.

The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic : Wiley. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these .

Quantile Regression. Quantile regression is a robust regression tool with critical advantages over OLS, including robustness to outliers, no normal distribution assumption, and quantification of relationships across the complete distribution of the dependent variable. From: Reconsidering Funds of Hedge Funds, Related terms: Regression Model. Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric.

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Book Description. Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric/5(10).

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 : Furno Marilena, Vistocco Domenico.

Jana Jureckova, Journal of the American Statistical Association "The author is one [of] the "fathers" of quantile regression. He has substantially contributed to the theoretical as well as the applied development of the field. The book is well written It provides useful information for statisticians and econometricians, Brand: Cambridge University Press.

Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models.

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions.

This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric/5(9). Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions.

This monograph is the first comprehensive treatment of the. Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions.

By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution.

Quantile Regression establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literatures exist for each subject matter, the authors explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.

Quantile regression models revealed effects of the type of urban area and gender in the potassium and sodium intakes, at least, in some quartiles, accounting for age, academic qualifications, and. Quantile Regression: The Movie Bivariate linear model with iid Student t errors Conditional quantile functions are parallelin blue observations indicated in blue Fitted quantile regression linesin red.

Intervals for ˝2(0,1) for which the solution is optimal. Roger Koenker (UIUC) Introduction Meielisalp: 15 / 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.

The book is a valuable contribution to the statistical literature, and a must have for every statistician or econometrician interested in quantile regression methods.' Journal of the Royal Statistical Society 'It is well written and easy to read, with useful ilustrations of important aspects of quantile regression /5(9).

QUANTILE TREATMENT EFFECTS The simplest formulation of regression is the classical two-sample treatment-control model. We begin by reconsidering a general model of two-sample treatment response introduced by Lehmann and Doksum in the by: 2. 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 as the software tools to implement the methods.

The main focus of this book is to provide the reader with a comprehensive description of the main. Quantile Regression, by Lingxin Hao and Daniel Q. Naiman, provides an excellent introduction to quantile-regression methods. The intuitive explanations and many examples make this book easy to read and understand.

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution.

We first interpret quantile-regression fits for specific quantiles. The median-regression quantile can be used to track location changes. Other specific regression quantiles, for example, theth andth quantiles, can be used to assess how a covariate.

To perform quantile regression in R we recommend the quantreg package, the versatile and mature package written by Roger Koenker, the guy who literally wrote the book on quantile regression. (Koenker, R. Quantile Regression, Cambridge U.

Press). Here’s how we perform the quantile regression that ggplot2 did for us using the. 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.

Quantile regression is a statistical technique intended to estimate, and conduct inference about, conditional quantile functions. Just as classical, linear regression methods based on minimizing sums of squared residuals enable one to estimate models for conditional mean functions, quantile regression methods offer a mechanism for estimating models for the conditional median function, and the full.

Outline 1. Exogenous Quantile Models 2. Quantile Regression 3. Applications 2 Cite as: Victor Chernozhukov, course materials for Nonlinear Econometric Analysis, Fall File Size: KB.This section is a stub.

You can help Wikibooks by expanding it. Quantile regression is a very old method which has become popular only in the last years thanks to computing progress.

One of the main researcher in this area is also a R practitioner and has developed a specific package for quantile regressions (quantreg) .There are many potential applications of quantile regression, e.g. growth analysis where patients and physicians are interested in percentiles, VaR where the statistic itself is a quantile metric.

In addition to this book, one may also take a look at the experimental version of quantile regression in SAS - the manual itself is pretty good also/5(7).