ArticleScandinavian journal of statistics, theory and applications2019
Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.
Article in Scandinavian journal of statistics, theory and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A modeling framework for detecting and leveraging node-level information in Bayesian network inference.Biostatistics (Oxford, England) · 2025Article
- Covariate-moderated Empirical Bayes Matrix Factorization.Advances in neural information processing systems · 2025Article
- Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- ComBat Harmonization: Empirical Bayes versus fully Bayes approaches.NeuroImage. Clinical · 2023Article
- Flexible co-data learning for high-dimensional prediction.Statistics in medicine · 2021Article
- Predictive and interpretable models via the stacked elastic net.Bioinformatics (Oxford, England) · 2021Article
- Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data.Biometrical journal. Biometrische Zeitschrift · 2021Article
- A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.The annals of applied statistics · 2020Article
- Biological and statistical interpretation of size-at-age, mixed-effects models of growth.Royal Society open science · 2020Article
- Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.Scandinavian journal of statistics, theory and applications · 2019Article
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3 authors.
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Abstract
Empirical Bayes is a versatile approach to "learn from a lot" in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well-known model-based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss "formal" empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross-validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed "co-data". In particular, we present two novel examples that allow for co-data: first, a Bayesian spike-and-slab setting that facilitates inclusion of multiple co-data sources and types and, second, a hybrid empirical Bayes-full Bayes ridge regression approach for estimation of the posterior predictive interval.
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