Simultaneous confidence interval for quantile regression
Author
Abstract
Suggested Citation
DOI: 10.1007/s00180-014-0537-7
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
- Hee-Seok Oh & Douglas W. Nychka & Thomas C. M. Lee, 2007. "The Role of Pseudo Data for Robust Smoothing with Application to Wavelet Regression," Biometrika, Biometrika Trust, vol. 94(4), pages 893-904.
- Krivobokova, Tatyana & Kneib, Thomas & Claeskens, Gerda, 2010. "Simultaneous Confidence Bands for Penalized Spline Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 852-863.
- Fenske, Nora & Kneib, Thomas & Hothorn, Torsten, 2011. "Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 494-510.
- Reiss Philip T. & Huang Lei, 2012. "Smoothness Selection for Penalized Quantile Regression Splines," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-27, May.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Italo R. Lima & Guanqun Cao & Nedret Billor, 2019. "M-based simultaneous inference for the mean function of functional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 577-598, June.
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.- Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019.
"Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP53/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 53/14, Institute for Fiscal Studies.
- Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
- Junshu Bao & Timothy Hanson & Garnett P. McMillan & Kristin Knight, 2017. "Assessment of DPOAE test-retest difference curves via hierarchical Gaussian processes," Biometrics, The International Biometric Society, vol. 73(1), pages 334-343, March.
- Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2018.
"Gold futures returns and realized moments: A forecasting experiment using a quantile-boosting approach,"
Resources Policy, Elsevier, vol. 57(C), pages 196-212.
- Matteo Bonato & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2016. "Gold Futures Returns and Realized Moments: A Forecasting Experiment Using a Quantile-Boosting Approach," Working Papers 201645, University of Pretoria, Department of Economics.
- Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016.
"Analysing farmland rental rates using Bayesian geoadditive quantile regression,"
European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(4), pages 663-698.
- März, Alexander & Klein, Nadja & Kneib, Thomas & Musshoff, Oliver, 2014. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182752, European Association of Agricultural Economists.
- März, Alexander & Klein, Nadja & Kneib, Thomas & Mußhoff, Oliver, 2014. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," DARE Discussion Papers 1403, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
- Roger Koenker, 2023. "Hotelling tubes, confidence bands and conformal inference," Empirical Economics, Springer, vol. 64(6), pages 2757-2769, June.
- Daouia, Abdelaati & Paindaveine, Davy, 2019. "Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression," TSE Working Papers 19-1022, Toulouse School of Economics (TSE), revised Feb 2023.
- Elisabeth Waldmann & Thomas Kneib & Yu Ryan Yu & Stefan Lang, 2012. "Bayesian semiparametric additive quantile regression," Working Papers 2012-06, Faculty of Economics and Statistics, Universität Innsbruck.
- Hofner, Benjamin & Mayr, Andreas & Schmid, Matthias, 2016. "gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i01).
- McGinnity, K. & Varbanov, R. & Chicken, E., 2017. "Cross-validated wavelet block thresholding for non-Gaussian errors," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 127-137.
- Yousuf, Kashif & Ng, Serena, 2021.
"Boosting high dimensional predictive regressions with time varying parameters,"
Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
- Kashif Yousuf & Serena Ng, 2019. "Boosting High Dimensional Predictive Regressions with Time Varying Parameters," Papers 1910.03109, arXiv.org.
- K. De Brabanter & Y. Liu & C. Hua, 2016. "Convergence rates for uniform confidence intervals based on local polynomial regression estimators," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 31-48, March.
- Monica Pratesi & M. Ranalli & Nicola Salvati, 2009. "Nonparametric -quantile regression using penalised splines," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(3), pages 287-304.
- Manuel Wiesenfarth & Carlos Matías Hisgen & Thomas Kneib & Carmen Cadarso-Suarez, 2014.
"Bayesian Nonparametric Instrumental Variables Regression Based on Penalized Splines and Dirichlet Process Mixtures,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 468-482, July.
- Manuel Wiesenfarth & Carlos Matías Hisgen & Thomas Kneib & Carmen Cadarso-Suarez, 2012. "Bayesian Nonparametric Instrumental Variable Regression based on Penalized Splines and Dirichlet Process Mixtures," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 127, Courant Research Centre PEG.
- Roland Langrock & Thomas Kneib & Alexander Sohn & Stacy L. DeRuiter, 2015. "Nonparametric inference in hidden Markov models using P-splines," Biometrics, The International Biometric Society, vol. 71(2), pages 520-528, June.
- Yang, Suigen & Xue, Liugen & Li, Gaorong, 2014. "Simultaneous confidence band for single-index random effects models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 6-14.
- Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
- Isabel Proença & Stefan Sperlich & Duygu Savaşcı, 2015. "Semi-mixed effects gravity models for bilateral trade," Empirical Economics, Springer, vol. 48(1), pages 361-387, February.
- Lu, Xiaolei & Kuriki, Satoshi, 2017. "Simultaneous confidence bands for contrasts between several nonlinear regression curves," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 83-104.
More about this item
Keywords
Penalized spline; Pseudo data; Quantile loss; Simultaneous confidence interval;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:compst:v:30:y:2015:i:2:p:345-358. 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.