6 edition of **Quantile Regression (Quantitative Applications in the Social Sciences)** found in the catalog.

- 291 Want to read
- 39 Currently reading

Published
**April 18, 2007** by Sage Publications, Inc .

Written in English

- Medicine,
- Research methods: general,
- Social research & statistics,
- Mathematics,
- Sociology,
- Science/Mathematics,
- Methodology,
- Research,
- Statistics,
- Social Science / Research,
- Probability & Statistics - Regression Analysis,
- Regression analysis,
- Social sciences,
- Statistical methods

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

Format | Paperback |

Number of Pages | 136 |

ID Numbers | |

Open Library | OL11716771M |

ISBN 10 | 1412926289 |

ISBN 10 | 9781412926287 |

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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.

Quantile Regression book 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 Quantile Regression book 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).

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 tools.

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 issues concerning quantile regression; these. 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.

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).

qreg can also estimate the regression plane for quantiles other than the (median). For instance, the following model describes the 25th percentile quantile) of price. qreg price weight length foreign, quantile) Iteration 1: WLS sum of weighted deviations = Iteration 1: sum of abs. weighted deviations = Iteration 2: sum of abs.

weighted deviations = 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 : Ebook.

Quantile Regression | Roger Koenker | download | B–OK. Download books for free. Find books. Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the Price: $ Quantile regression is a widely recognized useful alternative to the classical least-squares regression.

The most prominent feature of quantile regression is its ability to incorporate heterogeneity, which can arise from heteroskedastic variances or other sources beyond the commonly used location–scale : Lan Wang. Quantile Regression. Book. Seller Inventory # BBS More information about this seller | Contact this seller 9.

Quantile Regression (Econometric Society Monographs) Koenker, Roger. Published by Cambridge University Press () ISBN ISBN. Book Description. Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions.

Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared. The quantile level is the probability (or the proportion of the population) that is associated with a quantile.

The quantile level is often denoted by the Greek letter ˝, and the corresponding conditional quantile of Y given X is often written as Q ˝.YjX/.The quantile level ˝is the probability Pr„Y Q ˝.Y jX/X“, and it is the value of Y below which the File Size: 2MB. Quantile regression is used extensively in ecological studies (Cade and Noon ).

Recently, Dunham, Cade, and Terrell () applied quantile regression to analyze ﬁsh-habitat relationships for Lahontan cutthroat trout in 13 streams of the eastern Lahontan basin, which covers most of northern Nevada and parts of southern Oregon.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 .Quantile Regression (Econometric Society Monographs Book 38) eBook: Koenker, Roger: : Kindle Store/5(7).