IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v110y2015i509p233-245.html

Model-Robust Designs for Quantile Regression

Author

Listed:
  • Linglong Kong
  • Douglas P. Wiens

Abstract

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function, and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. The methods are illustrated by example, and in a case study in which they are applied to growth charts.

Suggested Citation

  • Linglong Kong & Douglas P. Wiens, 2015. "Model-Robust Designs for Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 233-245, March.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:509:p:233-245
    DOI: 10.1080/01621459.2014.969427
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2014.969427
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2014.969427?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. Wiens, Douglas P. & Wu, Eden K.H., 2010. "A comparative study of robust designs for M-estimated regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1683-1695, June.
    3. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    4. Wiens, Douglas P., 1991. "Designs for approximately linear regression: two optimality properties of uniform designs," Statistics & Probability Letters, Elsevier, vol. 12(3), pages 217-221, September.
    5. Pengfei Li & Douglas P. Wiens, 2011. "Robustness of design in dose–response studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 215-238, March.
    6. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    7. Biedermann, Stefanie & Dette, Holger, 2001. "Optimal designs for testing the functional form of a regression via nonparametric estimation techniques," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 215-224, April.
    8. Kaishev, V. K., 1989. "Optimal experimental designs for the B-spline regression," Computational Statistics & Data Analysis, Elsevier, vol. 8(1), pages 39-47, May.
    9. Holger Dette & Matthias Trampisch, 2012. "Optimal Designs for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1140-1151, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wen, Qianglin & Wu, Xiangkun & Shi, Chengchun & Li, Ting & Tang, Niansheng & Zhang, Yingying & Zhu, Hongtu, 2026. "Robust sequential experimental design for A/B testing," LSE Research Online Documents on Economics 138897, London School of Economics and Political Science, LSE Library.
    2. Peng, Cheng & Kouri, Drew P. & Uryasev, Stan, 2024. "Efficient and robust optimal design for quantile regression based on linear programming," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    3. Waite, Timothy W., 2024. "Replication in random translation designs," Statistics & Probability Letters, Elsevier, vol. 215(C).
    4. Kai Yzenbrandt & Julie Zhou, 2022. "Minimax robust designs for regression models with heteroscedastic errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 203-222, February.

    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.
    1. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
    2. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Spillovers from the United States to Latin American and G7 stock markets: A VAR quantile analysis," Emerging Markets Review, Elsevier, vol. 31(C), pages 32-46.
    3. González, María de la O & Jareño, Francisco, 2019. "Testing extensions of Fama & French models: A quantile regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 188-204.
    4. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    5. 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.
    6. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    7. 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.
    8. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    9. Kleopatra Nikolaou, 2007. "The behaviour of the real exchange rate: Evidence from regression quantiles," Money Macro and Finance (MMF) Research Group Conference 2006 46, Money Macro and Finance Research Group.
    10. Jean-Marc Fournier & Isabell Koske, 2012. "The determinants of earnings inequality: evidence from quantile regressions," OECD Journal: Economic Studies, OECD Publishing, vol. 2012(1), pages 7-36.
    11. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    12. Zhang, Yaowu & Zhou, Yeqing & Zhu, Liping, 2025. "Interval quantile correlations with applications to testing high-dimensional quantile effects," Journal of Econometrics, Elsevier, vol. 249(PA).
    13. de Castro, Luciano & Galvao, Antonio F. & Ota, Hirofumi, 2026. "Quantile approach to intertemporal consumption with multiple assets," Journal of Econometrics, Elsevier, vol. 253(C).
    14. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    15. Gökgöz, Fazıl & Yücel, Öykü, 2025. "Measuring the long-term impact of wind, run-of-river, solar renewable energy alternatives on market clearing prices," Renewable Energy, Elsevier, vol. 241(C).
    16. Min Ren & Shengli Zhao & Mingqiu Wang & Xinbei Zhu, 2024. "Robust optimal subsampling based on weighted asymmetric least squares," Statistical Papers, Springer, vol. 65(4), pages 2221-2251, June.
    17. Jamal Bouoiyour & Refk Selmi, 2017. "The Bitcoin price formation: Beyond the fundamental sources," Working Papers hal-01548710, HAL.
    18. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, IZA Network @ LISER.
    19. Chen, Xirong & Li, Degui & Li, Qi & Li, Zheng, 2019. "Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates," Journal of Econometrics, Elsevier, vol. 212(2), pages 433-450.
    20. Özkan, Oktay & Destek, Mehmet Akif & Balsalobre-Lorente, Daniel & Esmaeili, Parisa, 2024. "Unlocking the impact of international financial support to infrastructure, energy efficiency, and ICT on CO2 emissions in India," Energy Policy, Elsevier, vol. 194(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    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:taf:jnlasa:v:110:y:2015:i:509:p:233-245. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.