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.
Quantile Regression 作者: Cristina Davino / Marilena Furno / Domenico Vistocco 副标题: Theory and Applications 出版年: 2013-12-31 页数: 276 装帧: 精装 ISBN: 9781119975281 豆瓣评分 评价人数不足 评价: 写笔记 写书评 加入购书单 分享到 推荐 目录 ··· Preface ix Acknowledgments xi Intr...
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...
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...
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 ...
In the decades since the introduction of quantile regression, the potential of this method has been appreciated more and more and in the most diverse fields of application (see [32] and [47] for a review of the research areas). In addition to the numerous applications in the economic ...
There has been growing literature about quantile regression theory and applications. In application context, covariates are often measured with errors. Wei and Carroll (2009) stated that the ignoring measurement errors would lead to bias estimation. He and Liang (2000) analyzed the linear quantile ...
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 ...
A close link to rank-based inference has been forged from the theory of the dual regression quantile process, or regression rankscore process. Recent work has extended quantile regression into time-series, spatial models, survival analysis, and nonparametric estimation. Keywords: quantile regression; ...
existing in the very extended literature on quantile or expectile regression and estimation, coming from different fields such as Robust Statistics, Generalized Quantile Regression for functional data, Statistical Learning Theory, Non-parametric Smoothing and Regularization, Adaptive Semi-parametric Estimation....