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Confidence intervals and regions for the lasso by using stochastic variational inequality techniques in optimization

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  • Shu Lu
  • Yufeng Liu
  • Liang Yin
  • Kai Zhang

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  • Shu Lu & Yufeng Liu & Liang Yin & Kai Zhang, 2017. "Confidence intervals and regions for the lasso by using stochastic variational inequality techniques in optimization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 589-611, March.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:2:p:589-611
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    References listed on IDEAS

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    1. 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.
    2. Chatterjee, A. & Lahiri, S. N., 2011. "Bootstrapping Lasso Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 608-625.
    3. Lam, Clifford & Fan, Jianqing, 2009. "Sparsistency and rates of convergence in large covariance matrix estimation," LSE Research Online Documents on Economics 31540, London School of Economics and Political Science, LSE Library.
    4. Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
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    Cited by:

    1. Joel L. Horowitz & Ahnaf Rafi, 2023. "Bootstrap based asymptotic refinements for high-dimensional nonlinear models," CeMMAP working papers 06/23, Institute for Fiscal Studies.
    2. Miju Ahn, 2020. "Consistency bounds and support recovery of d-stationary solutions of sparse sample average approximations," Journal of Global Optimization, Springer, vol. 78(3), pages 397-422, November.

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