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Multilevel quantile function modeling with application to birth outcomes

Author

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  • Luke B. Smith
  • Brian J. Reich
  • Amy H. Herring
  • Peter H. Langlois
  • Montserrat Fuentes

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  • Luke B. Smith & Brian J. Reich & Amy H. Herring & Peter H. Langlois & Montserrat Fuentes, 2015. "Multilevel quantile function modeling with application to birth outcomes," Biometrics, The International Biometric Society, vol. 71(2), pages 508-519, June.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:508-519
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    File URL: http://hdl.handle.net/10.1111/biom.12294
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    References listed on IDEAS

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    1. Joshua Warren & Montserrat Fuentes & Amy Herring & Peter Langlois, 2012. "Spatial-Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure," Biometrics, The International Biometric Society, vol. 68(4), pages 1157-1167, December.
    2. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Lane F. Burgette & Jerome P. Reiter, 2012. "Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression," Biometrics, The International Biometric Society, vol. 68(1), pages 92-100, March.
    5. Huixia Judy Wang & Deyuan Li & Xuming He, 2012. "Estimation of High Conditional Quantiles for Heavy-Tailed Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1453-1464, December.
    6. Jien Chen & Nicole Lazar, 2010. "Quantile estimation for discrete data via empirical likelihood," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 237-255.
    7. Howard D. Bondell & Brian J. Reich & Huixia Wang, 2010. "Noncrossing quantile regression curve estimation," Biometrika, Biometrika Trust, vol. 97(4), pages 825-838.
    8. Dunson, David B. & Herring, Amy H. & Siega-Riz, Anna Maria, 2008. "Bayesian Inference on Changes in Response Densities Over Predictor Clusters," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1508-1517.
    9. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    10. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
    11. Wang, Hansheng & Tsai, Chih-Ling, 2009. "Tail Index Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1233-1240.
    12. Jan G. de Gooijer & Ao Yuan, 2011. "Kernel-Smoothed Conditional Quantiles of Correlated Bivariate Discrete Data," Tinbergen Institute Discussion Papers 11-011/4, Tinbergen Institute.
    13. Brian J. Reich & Luke B. Smith, 2013. "Bayesian Quantile Regression for Censored Data," Biometrics, The International Biometric Society, vol. 69(3), pages 651-660, September.
    14. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    15. Reich, Brian J. & Fuentes, Montserrat & Dunson, David B., 2011. "Bayesian Spatial Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 6-20.
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    Cited by:

    1. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    2. Luke B. Smith, 2016. "Discussion," International Statistical Review, International Statistical Institute, vol. 84(3), pages 359-362, December.
    3. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    4. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    5. Steven G. Xu & Brian J. Reich, 2023. "Bayesian nonparametric quantile process regression and estimation of marginal quantile effects," Biometrics, The International Biometric Society, vol. 79(1), pages 151-164, March.

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