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 85 articles )

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  1. Cheng, Chao; Feng, Xingdong; Huang, Jian; Liu, Xu: Regularized projection score estimation of treatment effects in high-dimensional quantile regression (2022)
  2. Yi, Sangyoon; Zhang, Xianyang: Projection-based inference for high-dimensional linear models (2022)
  3. Zhang, Dongliang; Khalili, Abbas; Asgharian, Masoud: Post-model-selection inference in linear regression models: an integrated review (2022)
  4. Bartlett, Peter L. (ed.); Butucea, Cristina (ed.); Schmidt-Hieber, Johannes (ed.): Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting) (2021)
  5. Dai, Ran; Kolar, Mladen: Inference for high-dimensional varying-coefficient quantile regression (2021)
  6. Hong, Liang; Martin, Ryan: Valid model-free prediction of future insurance claims (2021)
  7. Jeng, X. Jessie; Peng, Huimin; Lu, Wenbin: Model selection with mixed variables on the Lasso path (2021)
  8. Jin, Rui; Tan, Aixin: Fast Markov chain Monte Carlo for high-dimensional Bayesian regression models with shrinkage priors (2021)
  9. Li, Ning; Peng, Xiaoling; Kawaguchi, Eric; Suchard, Marc A.; Li, Gang: A scalable surrogate (L_0) sparse regression method for generalized linear models with applications to large scale data (2021)
  10. Qian, Min; Chakraborty, Bibhas; Maiti, Raju; Cheung, Ying Kuen: A sequential significance test for treatment by covariate interactions (2021)
  11. Xue, Kaijie; Yao, Fang: Hypothesis testing in large-scale functional linear regression (2021)
  12. Zhao, Bangxin; Liu, Xin; He, Wenqing; Yi, Grace Y.: Dynamic tilted current correlation for high dimensional variable screening (2021)
  13. Zheng, Zemin; Zhang, Jiarui; Li, Yang; Wu, Yaohua: Partitioned approach for high-dimensional confidence intervals with large split sizes (2021)
  14. Azaïs, Jean-Marc; De Castro, Yohann; Mourareau, Stéphane: Testing Gaussian process with applications to super-resolution (2020)
  15. Camponovo, Lorenzo: Bootstrap inference for penalized GMM estimators with oracle properties (2020)
  16. Li, Sai: Debiasing the debiased Lasso with bootstrap (2020)
  17. Lu, Junwei; Kolar, Mladen; Liu, Han: Kernel meets sieve: post-regularization confidence bands for sparse additive model (2020)
  18. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Rejoinder on: “Hierarchical inference for genome-wide association studies: a view on methodology with software” (2020)
  19. Sottile, Gianluca; Frumento, Paolo; Chiodi, Marcello; Bottai, Matteo: A penalized approach to covariate selection through quantile regression coefficient models (2020)
  20. Tardivel, Patrick J. C.; Servien, Rémi; Concordet, Didier: Simple expressions of the Lasso and SLOPE estimators in low-dimension (2020)

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