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Variable Selection

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Showing 126–150 of 566 papers

TitleStatusHype
Controlling false discoveries in high-dimensional situations: Boosting with stability selection—0
An Automatic Relevance Determination Prior Bayesian Neural Network for Controlled Variable Selection—0
A Theory of Dichotomous Valuation with Applications to Variable Selection—0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
Developing an ICU scoring system with interaction terms using a genetic algorithm—0
Differentially Private Variable Selection via the Knockoff Filter—0
Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance—0
Credit Ratings: Heterogeneous Effect on Capital Structure—0
Asymptotic properties for combined L_1 and concave regularization—0
Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models—0
Data-driven forecasting of solar irradiance—0
Data-Driven Constitutive Relation Reveals Scaling Law for Hydrodynamic Transport Coefficients—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset—0
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks—0
DECOrrelated feature space partitioning for distributed sparse regression—0
Decoupling Shrinkage and Selection for the Bayesian Quantile Regression—0
Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies—0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
Deep Learning: A Bayesian Perspective—0
A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random—0
Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems—0
Automatic Identification of Twin Zygosity in Resting-State Functional MRI—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
An ADMM approach for multi-response regression with overlapping groups and interaction effects—0
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