Estimation of variance components, heritability and the ridge penalty in high-dimensional generalized linear models
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Publication:5086341
DOI10.1080/03610918.2019.1646760OpenAlexW2967525428WikidataQ127389030 ScholiaQ127389030MaRDI QIDQ5086341
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Publication date: 5 July 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1902.02623
Related Items (3)
Bayesian ridge regression for survival data based on a vine copula-based prior ⋮ Fast marginal likelihood estimation of penalties for group-adaptive elastic net ⋮ Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients
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- On high-dimensional misspecified mixed model analysis in genome-wide association study
- Linear and generalized linear mixed models and their applications.
- Heritability estimation in high dimensional sparse linear mixed models
- Gene network reconstruction using global-local shrinkage priors
- L1Penalized Estimation in the Cox Proportional Hazards Model
- Hazard Regression for Interval-Censored Data with Penalized Spline
- MM Algorithms for Variance Components Models
- On Some Ridge Regression Estimators: An Empirical Comparisons
- Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
- Empirical Bayesian Estimators for a Poisson Process Propagated in Time
- Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
- The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
- Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions
- Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
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