R package covTest: Computes covariance test for adaptive linear modelling. This package computes covariance test for the lasso.Compute the covariance test significance testing in adaptive linear modelling. Can be used with LARS (lasso) for linear models, elastic net, binomial and Cox survival model.

References in zbMATH (referenced in 64 articles )

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  1. Azaïs, Jean-Marc; De Castro, Yohann; Mourareau, Stéphane: Testing Gaussian process with applications to super-resolution (2020)
  2. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Rejoinder on: “Hierarchical inference for genome-wide association studies: a view on methodology with software” (2020)
  3. Tardivel, Patrick J. C.; Servien, Rémi; Concordet, Didier: Simple expressions of the Lasso and SLOPE estimators in low-dimension (2020)
  4. Zhao, Yaqing; Bondell, Howard: Solution paths for the generalized Lasso with applications to spatially varying coefficients regression (2020)
  5. Antonelli, Joseph; Parmigiani, Giovanni; Dominici, Francesca: High-dimensional confounding adjustment using continuous Spike and Slab priors (2019)
  6. Barber, Rina Foygel; Candès, Emmanuel J.: A knockoff filter for high-dimensional selective inference (2019)
  7. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  8. Cohen, Arthur; Kolassa, John; Sackrowitz, Harold B.: Penalized likelihood and multiple testing (2019)
  9. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  10. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  11. Jeng, X. Jessie; Chen, Xiongzhi: Predictor ranking and false discovery proportion control in high-dimensional regression (2019)
  12. Liao, Lina; Park, Cheolwoo; Choi, Hosik: Penalized expectile regression: an alternative to penalized quantile regression (2019)
  13. Relión, Jesús D. Arroyo; Kessler, Daniel; Levina, Elizaveta; Taylor, Stephan F.: Network classification with applications to brain connectomics (2019)
  14. Rinaldo, Alessandro; Wasserman, Larry; G’sell, Max: Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (2019)
  15. Shi, Chengchun; Song, Rui; Chen, Zhao; Li, Runze: Linear hypothesis testing for high dimensional generalized linear models (2019)
  16. Tibshirani, Ryan J.; Rosset, Saharon: Excess optimism: how biased is the apparent error of an estimator tuned by SURE? (2019)
  17. Umezu, Yuta; Takeuchi, Ichiro: Selective inference via marginal screening for high dimensional classification (2019)
  18. 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)
  19. Zheng, Lili; Raskutti, Garvesh: Testing for high-dimensional network parameters in auto-regressive models (2019)
  20. Barber, Rina Foygel; Kolar, Mladen: ROCKET: robust confidence intervals via Kendall’s tau for transelliptical graphical models (2018)

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