hierNet

hierNet: A Lasso for Hierarchical Interactions. Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) ”A Lasso for Hierarchical Interactions.” Annals of Statistics. 41(3). 1111-1141.


References in zbMATH (referenced in 43 articles , 1 standard article )

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  1. Feng, Sanying; Zhang, Menghan; Tong, Tiejun: Variable selection for functional linear models with strong heredity constraint (2022)
  2. Wei, Linchuan; Gómez, Andrés; Küçükyavuz, Simge: Ideal formulations for constrained convex optimization problems with indicator variables (2022)
  3. Weng, Jiaying: Fourier transform sparse inverse regression estimators for sufficient variable selection (2022)
  4. Page, Garritt L.; Quintana, Fernando A.; Rosner, Gary L.: Discovering interactions using covariate informed random partition models (2021)
  5. Wang, Cheng; Jiang, Binyan; Zhu, Liping: Penalized interaction estimation for ultrahigh dimensional quadratic regression (2021)
  6. Antonelli, Joseph; Mazumdar, Maitreyi; Bellinger, David; Christiani, David; Wright, Robert; Coull, Brent: Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors (2020)
  7. Ferrari, Federico; Dunson, David B.: Identifying main effects and interactions among exposures using Gaussian processes (2020)
  8. Hui, Francis K. C.; Müller, Samuel; Welsh, A. H.: The LASSO on latent indices for regression modeling with ordinal categorical predictors (2020)
  9. Shen, Sumin; Zhang, Zhiyang; Deng, Xinwei: On design and analysis of funnel testing experiments in webpage optimization (2020)
  10. Tang, Cheng Yong; Fang, Ethan X.; Dong, Yuexiao: High-dimensional interactions detection with sparse principal Hessian matrix (2020)
  11. Tibshirani, Robert; Friedman, Jerome: A pliable Lasso (2020)
  12. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  13. Dong, Hongbo: On integer and MPCC representability of affine sparsity (2019)
  14. Dong, Hongbo; Ahn, Miju; Pang, Jong-Shi: Structural properties of affine sparsity constraints (2019)
  15. Lederer, Johannes; Yu, Lu; Gaynanova, Irina: Oracle inequalities for high-dimensional prediction (2019)
  16. Li, Yang; Liu, Jun S.: Robust variable and interaction selection for logistic regression and general index models (2019)
  17. Mak, Simon; Wu, C. F. Jeff: \textsfcmenet: A new method for bi-level variable selection of conditional main effects (2019)
  18. Sato, Toshiki; Takano, Yuichi; Nakahara, Takanobu: Investigating consumers’ store-choice behavior via hierarchical variable selection (2019)
  19. Tan, Kean Ming; Lu, Junwei; Zhang, Tong; Liu, Han: Layer-wise learning strategy for nonparametric tensor product smoothing spline regression and graphical models (2019)
  20. Tyagi, Hemant; Vybiral, Jan: Learning general sparse additive models from point queries in high dimensions (2019)

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