References in zbMATH (referenced in 65 articles )

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  1. Azaïs, Jean-Marc; De Castro, Yohann; Mourareau, Stéphane: Testing Gaussian process with applications to super-resolution (2020)
  2. Li, Sai: Debiasing the debiased Lasso with bootstrap (2020)
  3. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Rejoinder on: “Hierarchical inference for genome-wide association studies: a view on methodology with software” (2020)
  4. Tardivel, Patrick J. C.; Servien, Rémi; Concordet, Didier: Simple expressions of the Lasso and SLOPE estimators in low-dimension (2020)
  5. Zhao, Yaqing; Bondell, Howard: Solution paths for the generalized Lasso with applications to spatially varying coefficients regression (2020)
  6. Antonelli, Joseph; Parmigiani, Giovanni; Dominici, Francesca: High-dimensional confounding adjustment using continuous Spike and Slab priors (2019)
  7. Barber, Rina Foygel; Candès, Emmanuel J.: A knockoff filter for high-dimensional selective inference (2019)
  8. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  9. Cohen, Arthur; Kolassa, John; Sackrowitz, Harold B.: Penalized likelihood and multiple testing (2019)
  10. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  11. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  12. Jeng, X. Jessie; Chen, Xiongzhi: Predictor ranking and false discovery proportion control in high-dimensional regression (2019)
  13. Liao, Lina; Park, Cheolwoo; Choi, Hosik: Penalized expectile regression: an alternative to penalized quantile regression (2019)
  14. Relión, Jesús D. Arroyo; Kessler, Daniel; Levina, Elizaveta; Taylor, Stephan F.: Network classification with applications to brain connectomics (2019)
  15. Rinaldo, Alessandro; Wasserman, Larry; G’sell, Max: Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (2019)
  16. Shi, Chengchun; Song, Rui; Chen, Zhao; Li, Runze: Linear hypothesis testing for high dimensional generalized linear models (2019)
  17. Tibshirani, Ryan J.; Rosset, Saharon: Excess optimism: how biased is the apparent error of an estimator tuned by SURE? (2019)
  18. Umezu, Yuta; Takeuchi, Ichiro: Selective inference via marginal screening for high dimensional classification (2019)
  19. Verma, A.; Buonocore, R. J.; Di Matteo, T.: A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering (2019)
  20. Zheng, Lili; Raskutti, Garvesh: Testing for high-dimensional network parameters in auto-regressive models (2019)

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