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Statistical Learning with Sparsity: The Lasso and Generalizations

著者: Carl M. O’Brien - International Statistical Review 2016 被引用: 765

Peer Reviewed

Text as DataText as DataGeneralized sparsecovariance-based…Generalized sparse covariance-based estimationRegularization inHierarchical Time Serie…Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter DataA tutorial onregularized partial…A tutorial on regularized partial correlation networks.Day-ahead electricityprice forecasting with…Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworksA Survey on NonconvexRegularization-Based…A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningSparsity in OptimalRandomized…Sparsity in Optimal Randomized Classification TreesGeneralization Bounds inthe…Generalization Bounds in the Predict-Then-Optimize FrameworkFeature engineering andsymbolic regression…Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation dataVariable selection forNaïve Bayes…Variable selection for Naïve Bayes classificationCompressed sensing influorescence microscopyCompressed sensing in fluorescence microscopyNetwork analysis ofinternet addiction and…Network analysis of internet addiction and depression among Chinese college students during the COVID-19 pandemic: A longitudinal studyStatistical Learningwith Sparsity: The Lass…Statistical Learning with Sparsity: The Lasso and Generalizations中心の論文この論文を引用する論文古い新しい

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