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The Bayesian Adaptive Lasso Regression
Mathematical Biosciences, 2018Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance. However, it requires consistent initial estimates of the regression coefficients, which are generally not available in high dimensional settings.
Haithem Taha Mohammad Ali +1 more
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Relaxed Adaptive Lasso and Its Asymptotic Results
Symmetry, 2022 This article introduces a novel two-stage variable selection method to solve the common asymmetry problem between the response variable and its influencing factors.
Rufei Zhang
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Robust Regression Through the Huber’s Criterion and Adaptive Lasso Penalty
Electronic Journal of Statistics, 2011 International audienceThe Huber's Criterion is a useful method for robust regression. The adaptive least absolute shrinkage and selection operator (lasso) is a popular technique for simultaneous estimation and variable selection.
Sophie Lambert-Lacroix
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A note on adaptive group lasso
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hansheng Wang 0002, Chenlei Leng
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Using Lasso and Adaptive Lasso to Identify DIF in Multidimensional 2PL Models
Multivariate Behavioral Research, 2022Differential item functioning (DIF) analysis refers to procedures that evaluate whether an item's characteristic differs for different groups of persons after controlling for overall differences in performance. DIF is routinely evaluated as a screening step to ensure items behave the same across groups.
Chun Wang, Ruoyi Zhu, Gongjun Xu
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Manifold-Regularized Adaptive Lasso
2018Adaptive Lasso preserves oracle properties comparing to classical Lasso. It performs as well as if the true underlying model is provided in advance. In order to let feature subset selected by Adaptive Lasso preserve more local information, which is discriminative and benefit for classification, Manifold-regularized Adaptive Lasso (MrALasso) is proposed
Sibao Chen 0001 +2 more
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Improvement of Adaptive Lasso in Binary Quantile Regression
2021 International Symposium on Electrical, Electronics and Information Engineering, 2021In order to avoid the over-fitting of the model, the adaptive LASSO method was used to the variables selection of the binary quantile regression model. Bayesian method is use to construct the Gibbs sampling algorithm and the constraint condition that does not affect the predictive result is used to improve the stability of the sampling value.
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Directed Adaptive Graphical Lasso for causality inference
Neurocomputing, 2016Graphical lasso provides a general solution to reveal the indirect statistic dependence of multiple variables in the high dimensional space. Rather than the undirected relationships, a number of practical problems concern much about the causality between nodes in terms of directed links.
Zhiquan Ren +4 more
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2018
In this chapter, we define a general nonparametric estimator of a d-variate function valued parameter ψ0. This parameter is defined as a minimizer of an expectation of a loss function L(ψ)(O) that is guaranteed to converge to the true ψ0 at a rate faster than n−1∕4, for all dimensions d: \(\sqrt{ d_{0}(\psi _{n},\psi _{0})} = O_{P}(n^{-1/4-\alpha (d)/8}
Mark J. van der Laan, David Benkeser
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In this chapter, we define a general nonparametric estimator of a d-variate function valued parameter ψ0. This parameter is defined as a minimizer of an expectation of a loss function L(ψ)(O) that is guaranteed to converge to the true ψ0 at a rate faster than n−1∕4, for all dimensions d: \(\sqrt{ d_{0}(\psi _{n},\psi _{0})} = O_{P}(n^{-1/4-\alpha (d)/8}
Mark J. van der Laan, David Benkeser
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Adaptive Lasso for vector Multiplicative Error Models
Quantitative Finance, 2018In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), and we show that they provide asymptotic consistency in variable selection and the same efficiency as ...
Luca Cattivelli, Giampiero M. Gallo
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