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Quantile Regression
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  • Cited by 1308
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    von Gablenz, Petra Otto-Sobotka, Fabian and Holube, Inga 2018. Adjusting Expectations: Hearing Abilities in a Population-Based Sample Using an SSQ Short Form. Trends in Hearing, Vol. 22, Issue. , p. 233121651878483.

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    Li, Yunxia and Ding, Jiali 2018. Weighted composite quantile regression method via empirical likelihood for non linear models. Communications in Statistics - Theory and Methods, Vol. 47, Issue. 17, p. 4286.

    Wright, Daniel B. 2018. Estimating school effectiveness with student growth percentile and gain score models. Journal of Applied Statistics, p. 1.

    Xu, Hong-Xia Fan, Guo-Liang Chen, Zhen-Long and Wang, Jiang-Feng 2018. Weighted quantile regression and testing for varying-coefficient models with randomly truncated data. AStA Advances in Statistical Analysis,

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    Fan, Yali Tang, Yanlin and Zhu, Zhongyi 2018. Variable selection in censored quantile regression with high dimensional data. Science China Mathematics, Vol. 61, Issue. 4, p. 641.

    Vu, Ky Poirion, Pierre-Louis and Liberti, Leo 2018. Random Projections for Linear Programming. Mathematics of Operations Research,

    Li, Hongxia Zhang, Mingliang Wang, Xiangyan Ding, Xiao and Si, Jiwei 2018. The Central Executive Mediates the Relationship Between Children’s Approximate Number System Acuity and Arithmetic Strategy Utilization in Computational Estimation. Frontiers in Psychology, Vol. 9, Issue. ,

    Petrella, Lea Laporta, Alessandro G. and Merlo, Luca 2018. Cross-Country Assessment of Systemic Risk in the European Stock Market: Evidence from a CoVaR Analysis. Social Indicators Research,

    Chen, Wen-Yi Chang, Tsangyao and Lin, Yu-Hui 2018. Investigating the Persistence of Suicide in the United States: Evidence from the Quantile Unit Root Test. Social Indicators Research, Vol. 135, Issue. 2, p. 813.

    Yang, Ke Zhu, Liping and Xu, Wangli 2018. Adaptive composite quantile regressions and their asymptotic relative efficiency. Journal of Statistical Computation and Simulation, Vol. 88, Issue. 5, p. 900.

    Chavas, Jean-Paul Di Falco, Salvatore Adinolfi, Felice and Capitanio, Fabian 2018. Weather effects and their long-term impact on the distribution of agricultural yields: evidence from Italy. European Review of Agricultural Economics,

    De Backer, Mickaël Ghouch, Anouar El and Van Keilegom, Ingrid 2018. An Adapted Loss Function for Censored Quantile Regression. Journal of the American Statistical Association, p. 1.

    Barlow, Anna Maria Rohrbeck, Christian Sharkey, Paul Shooter, Rob and Simpson, Emma S. 2018. A Bayesian spatio-temporal model for precipitation extremes—STOR team contribution to the EVA2017 challenge. Extremes,

    Rogers, Megan L. and Joiner, Thomas E. 2018. Severity of Suicidal Ideation Matters: Reexamining Correlates of Suicidal Ideation Using Quantile Regression. Journal of Clinical Psychology, Vol. 74, Issue. 3, p. 442.

    Belloni, Alexandre Chernozhukov, Victor and Kato, Kengo 2018. Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models. Journal of the American Statistical Association, p. 1.

    Tian, Fengping Gao, Jiti and Yang, Ke 2018. A quantile regression approach to panel data analysis of health-care expenditure in Organisation for Economic Co-operation and Development countries. Health Economics,


Book description

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. 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 research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.


'… well written and easy to read, with useful illustrations of important aspects of quantile regression. It is obvious that the author knows the subject inside out, giving an up-to-date, exhaustive account of the subject. … The book is a valuable contribution to the statistical literature, and a must have for every statistician or econometrician interested in quantile regression methods.'

Source: Journal of the Royal Statistical Society

'It is well written and easy to read, with useful ilustrations of important aspects of quantile regression … a valuable contribution to the statistical literature and is essential for every statistician or econometrician who is interested in quantile regression methods.'

Andreas Karlsson - Uppsala University

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