Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression
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
- repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
- Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
- Jin, Hui & Rubin, Donald B., 2008. "Principal Stratification for Causal Inference With Extended Partial Compliance," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 101-111, March.
- Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Elisabeth Waldmann & Thomas Kneib & Yu Ryan Yu & Stefan Lang, 2012. "Bayesian semiparametric additive quantile regression," Working Papers 2012-06, Faculty of Economics and Statistics, University of Innsbruck.
- repec:eee:finlet:v:22:y:2017:i:c:p:35-41 is not listed on IDEAS
- Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016.
"Analysing farmland rental rates using Bayesian geoadditive quantile regression,"
European Review of Agricultural Economics,
Foundation for the European Review of Agricultural Economics, 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).
- Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
- 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.
- repec:jss:jstsof:v:074:i01 is not listed on IDEAS
- Yaeji Lim & Hee-Seok Oh, 2015. "Simultaneous confidence interval for quantile regression," Computational Statistics, Springer, vol. 30(2), pages 345-358, June.
- Fenske Nora & Fahrmeir Ludwig & Hothorn Torsten & Rzehak Peter & Höhle Michael, 2013. "Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-18, July.
- 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.
- Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013.
"Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models,"
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, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Zhao, Weihua & Lian, Heng & Song, Xinyuan, 2017. "Composite quantile regression for correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 15-33.
More about this item
StatisticsAccess and download statistics
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:bes:jnlasa:v:106:i:494:y:2011:p:494-510. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .
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.
We have no references for this item. You can help adding them by using 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.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.