Regression coefficient and autoregressive order shrinkage and selection via the lasso

By Hansheng Wang, Guodong Li, Chih-Ling Tsai. 2007

360 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/690313e34df78e840e772c78ed43fd492cd8057a

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Citations in the corpus

Efficient modeling and forecasting of electricity spot prices (2014, neutral): “would be outside the scope of this paper, the interested reader is referred to the works of Weron (2006) or Aggarwal et al.”
Regularization Parameter Selections via Generalized Information Criterion (2010, methodology): “Recently, Wang, Li, and Tsai (2007b) found that the resulting model selected by GCV tends to overfit, while BIC is able to identify the finite-dimensional true linear and partial linear models consistently.”
Penalized maximum likelihood estimation and variable selection in geostatistics (2011, background): “where k(λ) is the number of nonzero regression coefficients [19].”
Numerical construction of Green's functions in high dimensional elliptic problems with variable coefficients and analysis of renewable energy data via sparse and separable approximations (2012, methodology): “In the statistical inference literature, early stepwise-type algorithms paved the way for the now-familiar lasso [133], least angle regression (LARS) [48], and many variants tailored to specific problems (for example, [151, 155,”
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction (2016, methodology): “There are many parameter estimation techniques developed for AR with various regularizers [11, 20].”
Influential factors in crude oil price forecasting (2017, background): “It was first introduced by Tibshirani (1996) and has gradually found application in the energy area such as electricity consumption (Wang et al., 2007), electricity prices (Suard et al., 2010), and natural gas prices (Alfano et al., 2015).”
Large Vector Auto Regressions (2011, methodology): “Proof of Lemma 3.1 The proofs are closely built upon those of Wang et al. (2007).”
Gradient-based kernel method for feature extraction and variable selection (2012, background): “In recent years, extensive studies have been done on variable selection with a sparseness penalty ([6, 16, 18, 23–27, 29, 30] among many others).”
Performance bounds for parameter estimates of high-dimensional linear models with correlated errors (2016, background): “In terms of dependent errors, [41] proposed a Lasso estimator when the errors follow an autoregressive model.”
Shrinkage Estimation of the Varying Coefficient Model (2009, methodology): “To achieve the oracle property, the appropriate convergence rates for the shrinkage parameters have been well investigated (Fan and Li 2001; Zou 2006; Zhang and Lu 2007; Wang et al. 2007a).”
Least angle and ℓ1 penalized regression: A review (2008, methodology): “For LASSO, and to a greater extent for stagewise, variables can leave the model, and possibly re-enter later, multiple times.”
ADAPTIVE SEMI-VARYING COEFFICIENT MODEL SELECTION (2012, background): “…but are not limited to, the nonnegative garrotte (Breiman (1995); Yuan and Lin (2007)), the LASSO (Tibshirani (1996); Zou (2006); Zhang and Lu (2007); Wang, Li, and Tsai (2007)), bridge regression (Fu (1998); Knight and Fu (2000)), the SCAD (Fan and Li (2001)), and the one-step sparse estimator…”
Sparse Quantile Huber Regression for Efficient and Robust Estimation (2014, methodology): “In statistics, sparsity is often used to find the most parsimo n ous model that explains the data [39, 10, 33, 31].”
Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012, background): “For this reason Yuan and Lin (2006) introduced the group Lasso which penalizes different groups of variables differently.”
High dimensional ordinary least squares projection for screening variables (2015, background): “With the classical bound on the Gaussian tail, one can show that the Gaussian distribution admits a square-exponential tail in that q(t) = t(2)/2. This characterization of the tail behavior is an analog of Proposition 5.10 and 5.16 in Vershynin (2010) and is very general.”
Computational Statistics and Data Analysis a Note on Adaptive Group Lasso (neutral): “A theoretical justification which shows GCV overfits for smoothly clipped absolute deviation (Fan and Li, 2001, SCAD) method is given by Wang et al. (2007c).”