Computational Statistics and Data Analysis a Note on Adaptive Group Lasso

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Subfields: Artificial Intelligence, Rheumatology, Statistics and Probability

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Citations in the corpus

Variable selection for high-dimensional generalized varying-coefficient models (2012, methodology): “In particular, the adaptive group lasso was proposed in Wang and Leng (2008) and is the penalty we use here.”
Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (2016, unknown): “A combination of the adaptive Lasso and group Lasso yields the adaptive group Lasso that can achieve selection consistency for “grouped variables”; see, e.g., Wang and Leng (2008).”
CONSISTENT AND CONSERVATIVE MODEL SELECTION WITH THE ADAPTIVE LASSO IN STATIONARY AND NONSTATIONARY AUTOREGRESSIONS (2015, background): “We shall also make use of the fact that the least squares estimator, (ρ̂I , β̂ ′ I), of (ρ ∗, β∗′) in (1) of the main paper satisfies that ∥∥∥ST [(ρ̂I , β̂′ I)′ − (ρ∗, β∗′)′]∥∥∥ `2 ∈ Op(1) The idea of the proof is as in the proof of Theorem 2 in Zou (2006). Alternatively, one could follow the route ”
Regularized k-means clustering of high-dimensional data and its asymptotic consistency (2012, background): “Particularly, the regularization term J(C(j)) can be group LASSO penalty λ‖C(j)‖ [27], or adaptive group LASSO penalty J(C(j)) = λj‖C(j)‖ [22], where λ and λj , j = 1, .”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, methodology): “In the past two decades, there has been much work on penalization methods for variable selection and parameter estimation for high-dimensional data, including the bridge estimator (Frank and Friedman 1993); the least absolute shrinkage and selection operator (LASSO) (Tibshirani 1996) and its extensi”
Adaptive Group Sparse Regularization for Continual Learning (2020, methodology): “Namely, for the unimportant nodes in G(t−1)0 , we apply the ordinary group Lasso regularization (4) as in [3], and for the important nodes in G\G(t−1)0 , we apply the group-sparsity based penalty (5) that adaptively penalizes the deviation of θ`,n from the previously learned θ̂ (t−1) `,n depending o”
On Performance of Shrinkage Methods – A Monte Carlo Study (2015, methodology): “[9] developed Least Angle Regression Selection (LARS) for a model selection algorithm [10], [11] study the properties of adaptive group Lasso.”
Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions (2012, background): “…ridge regression [16], numerous lasso variations such as group lasso [17,18], which includes or excludes variables in groups, adaptive group lasso [19], lasso penalized generalized linear models [12], the Dantzig selector [20], a slightly modified version of the lasso, generalized elastic net…”
Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012, methodology): “Hence, inspired by Wang and Leng (2008) we combine the group Lasso of Yuan and Lin (2006) with the adaptive Lasso to make use of this grouping structure.”