References in zbMATH (referenced in 26 articles , 1 standard article )

Showing results 1 to 20 of 26.
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  1. Belloni, Alexandre; Hansen, Christian; Newey, Whitney: High-dimensional linear models with many endogenous variables (2022)
  2. Yi, Sangyoon; Zhang, Xianyang: Projection-based inference for high-dimensional linear models (2022)
  3. Zhu, Ke; Liu, Hanzhong: Confidence intervals for parameters in high-dimensional sparse vector autoregression (2022)
  4. Chernozhukov, Victor; Härdle, Wolfgang Karl; Huang, Chen; Wang, Weining: Lasso-driven inference in time and space (2021)
  5. Ciuperca, Gabriela: Variable selection in high-dimensional linear model with possibly asymmetric errors (2021)
  6. Guo, Zijian; Renaux, Claude; Bühlmann, Peter; Cai, Tony: Group inference in high dimensions with applications to hierarchical testing (2021)
  7. Hemerik, Jesse; Thoresen, Magne; Finos, Livio: Permutation testing in high-dimensional linear models: an empirical investigation (2021)
  8. Krampe, Jonas; Kreiss, Jens-Peter; Paparoditis, Efstathios: Bootstrap based inference for sparse high-dimensional time series models (2021)
  9. Wang, Rui; Xu, Xingzhong: A Bayesian-motivated test for high-dimensional linear regression models with fixed design matrix (2021)
  10. Deng, Hang; Zhang, Cun-Hui: Beyond Gaussian approximation: bootstrap for maxima of sums of independent random vectors (2020)
  11. Lee, Stephen M. S.; Yang, Puyudi: Bootstrap confidence regions based on M-estimators under nonstandard conditions (2020)
  12. Li, Sai: Debiasing the debiased Lasso with bootstrap (2020)
  13. Lopes, Miles E.; Lin, Zhenhua; Müller, Hans-Georg: Bootstrapping max statistics in high dimensions: near-parametric rates under weak variance decay and application to functional and multinomial data (2020)
  14. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)
  15. Das, Debraj; Gregory, Karl; Lahiri, S. N.: Perturbation bootstrap in adaptive Lasso (2019)
  16. Hansen, Christian; Liao, Yuan: The factor-Lasso and (k)-step bootstrap approach for inference in high-dimensional economic applications (2019)
  17. Jeng, X. Jessie; Chen, Xiongzhi: Predictor ranking and false discovery proportion control in high-dimensional regression (2019)
  18. Rinaldo, Alessandro; Wasserman, Larry; G’sell, Max: Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (2019)
  19. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  20. Gueuning, Thomas; Claeskens, Gerda: A high-dimensional focused information criterion (2018)

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