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Bent Line Quantile Regression with Application to an Allometric Study of Land Mammals' Speed and Mass

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  • Chenxi Li
  • Ying Wei
  • Rick Chappell
  • Xuming He

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  • Chenxi Li & Ying Wei & Rick Chappell & Xuming He, 2011. "Bent Line Quantile Regression with Application to an Allometric Study of Land Mammals' Speed and Mass," Biometrics, The International Biometric Society, vol. 67(1), pages 242-249, March.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:1:p:242-249
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01436.x
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    References listed on IDEAS

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Tests of Linear Hypotheses and l[subscript]1 Estimation," Econometrica, Econometric Society, vol. 50(6), pages 1577-1583, November.
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    Cited by:

    1. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    2. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    3. Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao, 2022. "Jiang, Zhao and Shao's reply to the Discussion of ‘The First Discussion Meeting on Statistical Aspects of the Covid‐19 Pandemic’," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1849-1854, October.
    4. Chenxi Li & N. Maritza Dowling & Rick Chappell, 2015. "Quantile regression with a change‐point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease," Biometrics, The International Biometric Society, vol. 71(3), pages 625-635, September.
    5. Zhang, Feipeng & Li, Qunhua, 2017. "A continuous threshold expectile model," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 49-66.
    6. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    7. Feipeng Zhang & Qunhua Li, 2023. "Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high‐throughput experiments," Biometrics, The International Biometric Society, vol. 79(3), pages 2272-2285, September.
    8. Chuang Wan & Wei Zhong & Wenyang Zhang & Changliang Zou, 2023. "Multikink quantile regression for longitudinal data with application to progesterone data analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 747-760, June.
    9. Songfeng Zheng, 2014. "A generalized Newton algorithm for quantile regression models," Computational Statistics, Springer, vol. 29(6), pages 1403-1426, December.
    10. Yanyang Yan & Feipeng Zhang & Xiaoying Zhou, 2017. "A note on estimating the bent line quantile regression model," Computational Statistics, Springer, vol. 32(2), pages 611-630, June.

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