In this talk we are going to discuss the theory, implementation, and application of quantile regression. We can say that least squares linear regression corresponds to finding the mean of a single distribution. Similarly, quantile regression corresponds to finding quantiles of a single distribution. ...
Quantile Regression: Theory and Applications 1st edn. (Wiley, 2014). 6. Koenker, R. Quantile Regression 1st edn. (Cambridge University Press, 2005). https://doi.org/10.1017/CBO9780511754098. 7. Zhang, L., Lv, X. & Wang, R. Soil moisture estimation based ...
Quantile Regression: Theory and Applications 1st edn. (Wiley, 2014). 6. Koenker, R. Quantile Regression 1st edn. (Cambridge University Press, 2005). https://doi.org/10.1017/CBO9780511754098. 7. Zhang, L., Lv, X. & Wang, R. Soil moisture estimation based ...
arbitrarily large (up to\(+\infty \)) and any observation below the plane can be made arbitrarily small (up to\(-\infty \)) without altering the fitted solution. This characteristic of quantile regression is also useful for the analysis of censored response variables, see Koenker and Portnoy...
[Wiley Series in Probability and Statistics] Quantile Regression (Theory and Applications) || Quantile regression C Davino,M Furno,D Vistocco 被引量: 0发表: 2014年 Simulation and comparison of Theil Sen Re-gression, Theil Sen Siegel Regression, and Quantile Regression with outliers Researchers in...
S. CADE (2000): "Paradigm Shift in Theory and Methods: Regression Quantile Analysis Enables New Insights for Ecology," in Proceedings of the 4th International Conference on Integrating Geographic Information Systems and Environmental Modeling: Problems, Prospects, and Needs for Research, ed...
It is an extension of median regression to examine the covariates' influence on different quan- tiles of the entire response distribution. Fixed effects could have different impacts across various quantile levels. QR allows for a wide range of applications, for example, investigating the 5th or ...
xed weighting approach and the predictive mean regression model. The improved out-of-sample performance of the robust point forecasts using time-varying weights over the predictive mean regression model is also apparent since most of the MSFE ratios for the individual predictors are lower than the ...
This strength of quantile regression has also been its liability. Most modern scientific applications of quantile regression involve a synthesis of estimates obtained at several quantile levels. Estimates and P-values are pooled together to build a composite picture of how predictors influence the respo...
Building upon the theory of Lu and Su (2015), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987). We check the finite sample performance via Monte Carlo simulations and empirical applications....