lars

R package lars: Least Angle Regression, Lasso and Forward Stagewise. Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 41 articles )

Showing results 1 to 20 of 41.
Sorted by year (citations)

1 2 3 next

  1. Ajmal, Hamda B.; Madden, Michael G.: Inferring dynamic gene regulatory networks with low-order conditional independencies -- an evaluation of the method (2020)
  2. Bedoui, Adel; Lazar, Nicole A.: Bayesian empirical likelihood for ridge and Lasso regressions (2020)
  3. Bob Obenchain: Ridge TRACE Diagnostics (2020) arXiv
  4. Martin, Ryan; Tang, Yiqi: Empirical priors for prediction in sparse high-dimensional linear regression (2020)
  5. Zhao, Yaqing; Bondell, Howard: Solution paths for the generalized Lasso with applications to spatially varying coefficients regression (2020)
  6. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Soltys, Michal Frej, Jan Mielniczuk: Improving Lasso for model selection and prediction (2019) arXiv
  7. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  8. Karl Sjöstrand; Line Clemmensen; Rasmus Larsen; Gudmundur Einarsson; Bjarne Ersbøll: SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling (2018) not zbMATH
  9. Pazira, Hassan; Augugliaro, Luigi; Wit, Ernst: Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter (2018)
  10. Adriano Zambom and Michael Akritas: NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection (2017) not zbMATH
  11. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  12. Lee, Youngjo; Ronnegård, Lars; Noh, Maengseok: Data analysis using hierarchical generalized linear models with R (2017)
  13. Oelker, Margret-Ruth; Tutz, Gerhard: A uniform framework for the combination of penalties in generalized structured models (2017)
  14. Pilanci, Mert; Wainwright, Martin J.: Newton sketch: a near linear-time optimization algorithm with linear-quadratic convergence (2017)
  15. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Robust groupwise least angle regression (2016)
  16. Beinrucker, Andre; Dogan, Ürün; Blanchard, Gilles: Extensions of stability selection using subsamples of observations and covariates (2016)
  17. Biau, Gérard; Fischer, Aurélie; Guedj, Benjamin; Malley, James D.: COBRA: a combined regression strategy (2016)
  18. Duncan, I.; Loginov, M.; Ludkovski, M.: Testing alternative regression frameworks for predictive modeling of health care costs (2016)
  19. Faisal, Muhammad; Futschik, Andreas; Hussain, Ijaz; Moemen, Mitwali Abd-El.: Choosing summary statistics by least angle regression for approximate Bayesian computation (2016)
  20. Fallahpour, Saber; Ejaz Ahmed, S.: Shrinkage estimation and variable selection in multiple regression models with random coefficient autoregressive errors (2014)

1 2 3 next