Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients
DOI10.1007/s00180-022-01213-8zbMath1505.62291OpenAlexW4220922516MaRDI QIDQ2095777
Hirofumi Michimae, Takeshi Emura
Publication date: 15 November 2022
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-022-01213-8
Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Bayesian inference (62F15) Characterization and structure theory for multivariate probability distributions; copulas (62H05)
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- Pair-copula constructions of multiple dependence
- Validation of Surrogate end Points in Multiple Randomized Clinical Trials with Failure Time end Points
- Stan
- Simplified pair copula constructions -- limitations and extensions
- Model selection via adaptive shrinkage with \(t\) priors
- Transformation of variables and the condition number in ridge estimation
- Derivatives and Fisher information of bivariate copulas
- An introduction to copulas.
- Robust ridge M-estimators with pretest and Stein-rule shrinkage for an intercept term
- Generating random correlation matrices based on vines and extended onion method
- Generating random correlation matrices based on partial correlations
- Some priors for sparse regression modelling
- On the simplified pair-copula construction -- simply useful or too simplistic?
- Bayesian copula selection
- A parameterization of positive definite matrices in terms of partial correlation vines
- Penalized likelihood and Bayesian function selection in regression models
- Hierarchical shrinkage priors for regression models
- Inference from iterative simulation using multiple sequences
- Analyzing dependent data with vine copulas. A practical guide with R
- Vines -- a new graphical model for dependent random variables.
- Prediction based on conditional distributions of vine copulas
- A Bayesian perspective of statistical machine learning for big data
- Preliminary test and Stein-type shrinkage ridge estimators in robust regression
- An iterative approach to minimize the mean squared error in ridge regression
- On the half-Cauchy prior for a global scale parameter
- Elastic Net Regression Modeling With the Orthant Normal Prior
- Uncertainty Analysis with High Dimensional Dependence Modelling
- A generalization and Bayesian interpretation of ridge-type estimators with good prior means
- The Bayesian Lasso
- A ridge-type estimator and good prior means
- Bayesian Variable Selection in Linear Regression
- Ridge regression:some simulations
- A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence
- A Bayesian approach with generalized ridge estimation for high-dimensional regression and testing
- The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
- Bayesian variable selection with related predictors
- Estimation of variance components, heritability and the ridge penalty in high-dimensional generalized linear models
- Examination and visualisation of the simplifying assumption for vine copulas in three dimensions
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Beta ridge regression estimators: simulation and application
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