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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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6 results for “co-data”

ecpc: An R-package for generic co-data models for high-dimensional predictionstat.ME

ecpc R-package improves high-dimensional prediction with co-data.

problem High-dimensional prediction with more variables than samples.

method Adaptive ridge penalised models with co-data, including continuous co-data.

result Improved variable selection and prediction performance.

high-dimensional prediction co-data variable selection ridge penalties elastic net

2022-05-16abs ↗pdf ↗

Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selectionstat.ME

Guided adaptive shrinkage uses co-data to improve feature selection in genomic studies.

problem Feature selection challenges in high-dimensional genomics data, especially in clinical settings.

method Guided adaptive shrinkage methods that use co-data to adapt shrinkage parameters.

result Improves feature selection in genomic studies, demonstrated through comparisons and examples.

genomics feature selection co-data shrinkage spike-and-slab

2024-05-08abs ↗pdf ↗

Flexible co-data learning for high-dimensional predictionstat.ME

Flexible co-data learning improves clinical prediction models.

problem High-dimensional clinical data challenges prediction accuracy.

method Combining domain knowledge and external studies to estimate adaptive multi-group ridge penalties.

result Improves prediction performance and variable selection stability.

clinical prediction high-dimensional data co-data ridge penalties adaptive shrinkage

2020-05-08abs ↗pdf ↗

Co-data Learning for Bayesian Additive Regression Treesstat.ML

Proposes using external data to improve predictions in medical applications with limited samples.

problem Small sample sizes and complex covariate-response relationships in medical data.

method Integrates external co-data into Bayesian Additive Regression Trees (BART) using an empirical Bayes framework.

result Improves prediction accuracy compared to standard BART, especially for nonlinear relationships.

Bayesian additive regression trees Empirical Bayes Co-data High-dimensional data Medical prediction

2023-11-16abs ↗pdf ↗

Fast marginal likelihood estimation of penalties for group-adaptive elastic netstat.ME

A fast method estimates group-adaptive elastic net penalties using co-data.

problem Computational inefficiency in estimating group-adaptive elastic net penalties.

method Derive low-dimensional representation of Taylor approximation for marginal likelihood and its derivative for group-adaptive ridge penalties; approximate elastic net marginal likelihood by ridge; transform ridge penalties to elastic net penalties.

result Significantly decreases computation time and outperforms other methods.

group-adaptive elastic net co-data fast method penalty estimation asymptotic normality

2021-01-11abs ↗pdf ↗

Penalised regression with multiple sources of prior effectsstat.ME

The paper proposes a method to integrate prior information into penalized regression.

problem Improving predictive performance in high-dimensional tasks with prior information.

method Integrating multiple sources of prior information into penalized regression.

result The method improves predictive performance, as shown by simulations and applications.

penalized regression prior information epigenetic markers R package

2022-12-16abs ↗pdf ↗