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 70 articles )

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  1. Zhao, Bangxin; Liu, Xin; He, Wenqing; Yi, Grace Y.: Dynamic tilted current correlation for high dimensional variable screening (2021)
  2. Azaïs, Jean-Marc; De Castro, Yohann; Mourareau, Stéphane: Testing Gaussian process with applications to super-resolution (2020)
  3. Li, Sai: Debiasing the debiased Lasso with bootstrap (2020)
  4. Lu, Junwei; Kolar, Mladen; Liu, Han: Kernel meets sieve: post-regularization confidence bands for sparse additive model (2020)
  5. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Rejoinder on: “Hierarchical inference for genome-wide association studies: a view on methodology with software” (2020)
  6. Sottile, Gianluca; Frumento, Paolo; Chiodi, Marcello; Bottai, Matteo: A penalized approach to covariate selection through quantile regression coefficient models (2020)
  7. Tardivel, Patrick J. C.; Servien, Rémi; Concordet, Didier: Simple expressions of the Lasso and SLOPE estimators in low-dimension (2020)
  8. Yu, Guan; Yin, Liang; Lu, Shu; Liu, Yufeng: Confidence intervals for sparse penalized regression with random designs (2020)
  9. Zhao, Yaqing; Bondell, Howard: Solution paths for the generalized Lasso with applications to spatially varying coefficients regression (2020)
  10. Antonelli, Joseph; Parmigiani, Giovanni; Dominici, Francesca: High-dimensional confounding adjustment using continuous Spike and Slab priors (2019)
  11. Barber, Rina Foygel; Candès, Emmanuel J.: A knockoff filter for high-dimensional selective inference (2019)
  12. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  13. Cohen, Arthur; Kolassa, John; Sackrowitz, Harold B.: Penalized likelihood and multiple testing (2019)
  14. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  15. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  16. Jeng, X. Jessie; Chen, Xiongzhi: Predictor ranking and false discovery proportion control in high-dimensional regression (2019)
  17. Liao, Lina; Park, Cheolwoo; Choi, Hosik: Penalized expectile regression: an alternative to penalized quantile regression (2019)
  18. Relión, Jesús D. Arroyo; Kessler, Daniel; Levina, Elizaveta; Taylor, Stephan F.: Network classification with applications to brain connectomics (2019)
  19. Rinaldo, Alessandro; Wasserman, Larry; G’sell, Max: Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (2019)
  20. Shi, Chengchun; Song, Rui; Chen, Zhao; Li, Runze: Linear hypothesis testing for high dimensional generalized linear models (2019)

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