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High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis

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  • Z. John Daye
  • Jinbo Chen
  • Hongzhe Li

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  • Z. John Daye & Jinbo Chen & Hongzhe Li, 2012. "High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis," Biometrics, The International Biometric Society, vol. 68(1), pages 316-326, March.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:1:p:316-326
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01652.x
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. P. Tseng, 2001. "Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization," Journal of Optimization Theory and Applications, Springer, vol. 109(3), pages 475-494, June.
    3. Wang, Hansheng & Li, Guodong & Jiang, Guohua, 2007. "Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 347-355, July.
    4. She, Yiyuan & Owen, Art B., 2011. "Outlier Detection Using Nonconvex Penalized Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 626-639.
    5. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    6. Jinfeng Xu & Zhiliang Ying, 2010. "Simultaneous estimation and variable selection in median regression using Lasso-type penalty," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(3), pages 487-514, June.
    7. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    Cited by:

    1. Luo, Bin & Gao, Xiaoli, 2022. "High-dimensional robust approximated M-estimators for mean regression with asymmetric data," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    2. repec:cte:wsrepe:24534 is not listed on IDEAS
    3. Hokeun Sun & Hongzhe Li, 2012. "Robust Gaussian Graphical Modeling Via l 1 Penalization," Biometrics, The International Biometric Society, vol. 68(4), pages 1197-1206, December.
    4. Wang, Xia & Shojaie, Ali & Zou, Jian, 2019. "Bayesian hidden Markov models for dependent large-scale multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 123-136.
    5. Chiou, Hai-Tang & Guo, Meihui & Ing, Ching-Kang, 2020. "Variable selection for high-dimensional regression models with time series and heteroscedastic errors," Journal of Econometrics, Elsevier, vol. 216(1), pages 118-136.
    6. Zhang, Lyuou & Zhou, Wen & Wang, Haonan, 2021. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    7. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.

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