Variable Selection
127 papers with code • 0 benchmarks • 0 datasets
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Outcome-adaptive lasso: variable selection for causal inference
Traditionally, a “throw in the kitchen sink” approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators.
Cross-Validation with Confidence
Cross-validation is one of the most popular model selection methods in statistics and machine learning.
An Expectation Conditional Maximization approach for Gaussian graphical models
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information.
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets.
Sparse quadratic classification rules via linear dimension reduction
We consider the problem of high-dimensional classification between the two groups with unequal covariance matrices.
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression.
Variable Selection and Task Grouping for Multi-Task Learning
We consider multi-task learning, which simultaneously learns related prediction tasks, to improve generalization performance.
Semi-Analytic Resampling in Lasso
An approximate method for conducting resampling in Lasso, the $\ell_1$ penalized linear regression, in a semi-analytic manner is developed, whereby the average over the resampled datasets is directly computed without repeated numerical sampling, thus enabling an inference free of the statistical fluctuations due to sampling finiteness, as well as a significant reduction of computational time.
Distributed Multivariate Regression Modeling For Selecting Biomarkers Under Data Protection Constraints
To minimize the amount of transferred data and the number of calls, we also provide a heuristic variant of the approach.
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
In spite of the usefulness of $L_0$-based estimators and generic MIO solvers, there is a steep computational price to pay when compared to popular sparse learning algorithms (e. g., based on $L_1$ regularization).
