Variable Selection
126 papers with code • 0 benchmarks • 0 datasets
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Flexible variable selection in the presence of missing data
Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression approaches in cases where a generalized linear model is misspecified.
Scalable Spike-and-Slab
Spike-and-slab priors are commonly used for Bayesian variable selection, due to their interpretability and favorable statistical properties.
OmicSelector: automatic feature selection and deep learning modeling for omic experiments
kstawiski/OmicSelector
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bioRxiv 2022
A crucial phase of modern biomarker discovery studies is selecting the most promising features from high-throughput screening assays.
Bayesian Variable Selection in a Million Dimensions
Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty.
Covariance and PCA for Categorical Variables
Covariances from categorical variables are defined using a regular simplex expression for categories.
Bayesian Approximate Kernel Regression with Variable Selection
State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively.
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression
Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task.
Improving SAT Solvers via Blocked Clause Decomposition
Our experiments on application benchmarks demonstrate that the new variables selection policy based on BCD can increase the performance of SAT solvers such as abcdSAT.
Boosting Joint Models for Longitudinal and Time-to-Event Data
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times.
metboost: Exploratory regression analysis with hierarchically clustered data
A machine learning method called boosted decision trees (Friedman, 2001) is a good approach for exploratory regression analysis in real data sets because it can detect predictors with nonlinear and interaction effects while also accounting for missing data.
