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 residuals, median regression minimizes sums of
Read Handbook of Quantile Regression (Chapman & Hall/CRC Handbooks of Modern Statistical Methods) - Roger Koenker file in PDF
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Sample selection in quantile regression: a survey (with stéphane bonhomme) september 2016. Prepared for the handbook of quantile regression, edited by roger koenker, victor chernozhukov, xuming he, and limin peng. This paper contains the code for the selection-corrected quantile regression estimator.
Chapter seven examines regression discontinuity and pipeline methods, which exploit the design of the program itself as potential sources of identification of program impacts. Specifically, chapter eight presents a discussion of how distributional impacts of programs can be measured, including new techniques related to quantile regression.
Handbook of statistics volume 30, 2012, pages 213- this chapter considers a wide range of time series quantile regression models.
In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods. Regression analysis seeks to find the relationship between one or more independent variables and a dependent variable.
One approach, distributional regression (dr), is based on direct estimation of the conditional distribution function; the other approach, quantile regression (qr),.
Jul 19, 2020 quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest handbook of quantile regression.
Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions.
Lemieux (2009): unconditional quantile regressions, econometrica, 77(3), 953-973.
Quantile regression, the first book of hao and naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile.
A statistical downscaling approach for extremes using censored quantile regression is presented.
Python for data analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in python. It is also a practical, modern introduction to scientific computing - selection from python for data analysis [book].
Quantile regression applications in ecology and the environmental sciences.
Quantile regression offers a variety of techniques for making inferences about conditional quantile functions.
By koenker and bassett (1978), extends the regression model to conditional quantiles of the response variable, such as the 90th percentile.
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