Papers Variable Selection
“Variable Selection” 태그가 달린 논문 566편 · 필터 해제
Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach
This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader econom…
Variable SelectionOn the ability of Deep Neural Networks to Learn Granger Causality in Multi-Variate Time Series Data
Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Linear Vector Autoregressive models (VAR) though have nice interpretation properties but have…
Time SeriesVariable SelectionBayesian Hybrid Machine Learning of Gallstone Risk
Gallstone disease is a complex, multifactorial condition with significant global health burdens. Identifying underlying risk factors and their interactions is crucial for early diagnosis, targeted prevention, and effecti…
Decision MakingHybrid Machine LearningregressionVariable SelectionGradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatia…
Model SelectionregressionVariable SelectionOn feature selection in double-imbalanced data settings: a Random Forest approach
Feature selection is a critical step in high-dimensional classification tasks, particularly under challenging conditions of double imbalance, namely settings characterized by both class imbalance in the response variable…
feature selectionVariable SelectionKnockoffs Inference under Privacy Constraints
Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-fre…
Variable SelectionHandling bounded response in high dimensions: a Horseshoe prior Bayesian Beta regression approach
Bounded continuous responses -- such as proportions -- arise frequently in diverse scientific fields including climatology, biostatistics, and finance. Beta regression is a widely adopted framework for modeling such data…
regressionVariable SelectionStability Selection via Variable Decorrelation
The Lasso is a prominent algorithm for variable selection. However, its instability in the presence of correlated variables in the high-dimensional setting is well-documented. Although previous research has attempted to …
Variable SelectionA Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
Model-based clustering integrated with variable selection is a powerful tool for uncovering latent structures within complex data. However, its effectiveness is often hindered by challenges such as identifying relevant v…
ClusteringComputational EfficiencyVariable SelectionGeneralization Guarantees for Learning Branch-and-Cut Policies in Integer Programming
Mixed-integer programming (MIP) provides a powerful framework for optimization problems, with Branch-and-Cut (B&C) being the predominant algorithm in state-of-the-art solvers. The efficiency of B&C critically depends on …
Sequential Decision MakingVariable SelectionNonparametric learning of covariate-based Markov jump processes using RKHS techniques
We propose a novel nonparametric approach for linking covariates to Continuous Time Markov Chains (CTMCs) using the mathematical framework of Reproducing Kernel Hilbert Spaces (RKHS). CTMCs provide a robust framework for…
Variable SelectionCFD-Based Quantification of Hemodynamic Variables in Cerebral Aneurysms: How Hemodynamics Shape Aneurysm Fate
Cerebral aneurysms are pathological dilations of intracranial arteries that can rupture with devastating consequences, including subarachnoid hemorrhage, stroke, and death. Accumulating evidence indicates that local hemo…
Variable SelectionConditional independence testing with a single realization of a multivariate nonstationary nonlinear time series
Identifying relationships among stochastic processes is a key goal in disciplines that deal with complex temporal systems, such as economics. While the standard toolkit for multivariate time series analysis has many adva…
Causal DiscoveryTime SeriesTime Series AnalysisVariable SelectionRidge partial correlation screening for ultrahigh-dimensional data
Variable selection in ultrahigh-dimensional linear regression is challenging due to its high computational cost. Therefore, a screening step is usually conducted before variable selection to significantly reduce the dime…
regressionVariable SelectionEnhancing Variable Selection in Large-scale Logistic Regression: Leveraging Manual Labeling with Beneficial Noise
In large-scale supervised learning, penalized logistic regression (PLR) effectively addresses the overfitting problem by introducing regularization terms yet its performance still depends on efficient variable selection …
Distributed ComputingVariable SelectionTransformer Learns Optimal Variable Selection in Group-Sparse Classification
Transformers have demonstrated remarkable success across various applications. However, the success of transformers have not been understood in theory. In this work, we give a case study of how transformers can be traine…
Variable SelectionHigh Dimensional Bayesian Optimization using Lasso Variable Selection
Bayesian optimization (BO) is a leading method for optimizing expensive black-box optimization and has been successfully applied across various scenarios. However, BO suffers from the curse of dimensionality, making it c…
Bayesian OptimizationComputational EfficiencyVariable SelectionBayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge …
PrognosisSurvival PredictionVariable SelectionCausal Feature Learning in the Social Sciences
Variable selection poses a significant challenge in causal modeling, particularly within the social sciences, where constructs often rely on inter-related factors such as age, socioeconomic status, gender, and race. Inde…
Causal InferenceVariable SelectionSpike-and-Slab Posterior Sampling in High Dimensions
Posterior sampling with the spike-and-slab prior [MB88], a popular multimodal distribution used to model uncertainty in variable selection, is considered the theoretical gold standard method for Bayesian sparse linear re…
Variable Selection