Multi-round smoothed composite quantile regression for distributed data
Author
Abstract
Suggested Citation
DOI: 10.1007/s10463-021-00816-0
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Joel L. Horowitz, 1998.
"Bootstrap Methods for Median Regression Models,"
Econometrica, Econometric Society, vol. 66(6), pages 1327-1352, November.
- Joel L. Horowitz, 1996. "Bootstrap Methods for Median Regression Models," Econometrics 9608004, University Library of Munich, Germany.
- Kaplan, David M. & Sun, Yixiao, 2017.
"Smoothed Estimating Equations For Instrumental Variables Quantile Regression,"
Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
- Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
- David M. Kaplan & Yixiao Sun, 2016. "Smoothed estimating equations for instrumental variables quantile regression," Papers 1609.09033, arXiv.org.
- David M. Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
- Jiang, Rong & Qian, Wei-Min & Zhou, Zhan-Gong, 2016. "Weighted composite quantile regression for single-index models," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 34-48.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Shi, Chengchun & Lu, Wenbin & Song, Rui, 2018. "A massive data framework for M-estimators with cubic-rate," LSE Research Online Documents on Economics 102111, London School of Economics and Political Science, LSE Library.
- Whang, Yoon-Jae, 2006.
"Smoothed Empirical Likelihood Methods For Quantile Regression Models,"
Econometric Theory, Cambridge University Press, vol. 22(2), pages 173-205, April.
- Yoon-Jae Whang, 2003. "Smoothed Empirical Likelihood Methods for Quantile Regression Models," Econometrics 0310005, University Library of Munich, Germany.
- Yoon-Jae Whang, 2004. "Smoothed Empirical Likelihood Methods for Quantile Regression Models," Cowles Foundation Discussion Papers 1453, Cowles Foundation for Research in Economics, Yale University.
- Runze Li & Dennis K.J. Lin & Bing Li, 2013. "Statistical inference in massive data sets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(5), pages 399-409, September.
- Bo Kai & Runze Li & Hui Zou, 2010. "Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 49-69, January.
- Chengchun Shi & Wenbin Lu & Rui Song, 2018. "A Massive Data Framework for M-Estimators with Cubic-Rate," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1698-1709, October.
- Heller, Glenn, 2007. "Smoothed Rank Regression With Censored Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 552-559, June.
- Jiang, Xuejun & Li, Jingzhi & Xia, Tian & Yan, Wanfeng, 2016. "Robust and efficient estimation with weighted composite quantile regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 413-423.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019.
"Smoothed GMM for quantile models,"
Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2018. "Smoothed GMM for quantile models," Working Papers 1803, Department of Economics, University of Missouri.
- Rong Jiang & Wei-wei Chen & Xin Liu, 2021. "Adaptive quantile regressions for massive datasets," Statistical Papers, Springer, vol. 62(4), pages 1981-1995, August.
- He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
- Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.
- Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021.
"Smoothing Quantile Regressions,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
- Fernandes, Marcelo & Guerre, Emmanuel & Horta, Eduardo, 2017. "Smoothing quantile regressions," Textos para discussão 457, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
- Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2019. "Smoothing quantile regressions," Papers 1905.08535, arXiv.org, revised Aug 2019.
- Tae-Hwy Lee & Aman Ullah & He Wang, 2023. "The Second-order Bias and Mean Squared Error of Quantile Regression Estimators," Working Papers 202313, University of California at Riverside, Department of Economics.
- de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019.
"Smoothed GMM for quantile models,"
Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
- Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2018. "Smoothed GMM for quantile models," Working Papers 1803, Department of Economics, University of Missouri.
- de Castro, Luciano & Cundy, Lance D. & Galvao, Antonio F. & Westenberger, Rafael, 2023. "A dynamic quantile model for distinguishing intertemporal substitution from risk aversion," European Economic Review, Elsevier, vol. 159(C).
- Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
- Parente, Paulo M.D.C. & Smith, Richard J., 2011.
"Gel Methods For Nonsmooth Moment Indicators,"
Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
- Paulo Parente & Richard Smith, 2008. "GEL methods for non-smooth moment indicators," CeMMAP working papers CWP19/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Kaplan, David M. & Sun, Yixiao, 2017.
"Smoothed Estimating Equations For Instrumental Variables Quantile Regression,"
Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
- Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
- David M. Kaplan & Yixiao Sun, 2016. "Smoothed estimating equations for instrumental variables quantile regression," Papers 1609.09033, arXiv.org.
- David M. Kaplan & Yixiao Sun, 2013. "Smoothed Estimating Equations for Instrumental Variables Quantile Regression," Working Papers 1314, Department of Economics, University of Missouri.
- Hong-Xia Xu & Guo-Liang Fan & Zhen-Long Chen & Jiang-Feng Wang, 2018. "Weighted quantile regression and testing for varying-coefficient models with randomly truncated data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 565-588, October.
- Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
- Yang, Jing & Tian, Guoliang & Lu, Fang & Lu, Xuewen, 2020. "Single-index modal regression via outer product gradients," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
- Bruins, Marianne & Duffy, James A. & Keane, Michael P. & Smith, Anthony A., 2018.
"Generalized indirect inference for discrete choice models,"
Journal of Econometrics, Elsevier, vol. 205(1), pages 177-203.
- Anthony A. Smith, Jr. & Michael Keane, 2004. "Generalized Indirect Inference for Discrete Choice Models," Econometric Society 2004 North American Winter Meetings 512, Econometric Society.
- Marianne Bruins & James A. Duffy & Michael P. Keane & Anthony A. Smith, Jr, 2015. "Generalized Indirect Inference for Discrete Choice Models," Economics Papers 2015-W08, Economics Group, Nuffield College, University of Oxford.
- Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
- Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
- Xiaofeng Lv & Rui Li, 2013. "Smoothed empirical likelihood analysis of partially linear quantile regression models with missing response variables," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 317-347, October.
- Sottile, Gianluca & Frumento, Paolo, 2022. "Robust estimation and regression with parametric quantile functions," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
- Escanciano, Juan Carlos & Velasco, Carlos, 2010.
"Specification tests of parametric dynamic conditional quantiles,"
Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
- Juan Carlos Escanciano & Carlos Velasco, 2008. "Specification Tests of Parametric Dynamic Conditional Quantiles," CAEPR Working Papers 2008-021, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
- J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
More about this item
Keywords
Bahadur representation; Composite quantile regression; Divide-and-conquer; Multiple rounds; Kernel smoothing; Weighted composite quantile regression;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aistmt:v:74:y:2022:i:5:d:10.1007_s10463-021-00816-0. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.