Confidence interval for normal means in meta-analysis based on a pretest estimator

Taketomi, Nanami; Chang, Yuan-Tsung; Konno, Yoshihiko; Mori, Mihoko; Emura, Takeshi

doi:10.1007/s42081-023-00221-2

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…To overcome this difficulty, Yang and Emura [15] suggested reducing the number of shrinkage parameters for the case of p > n in their formulation of a generalized ridge estimator. The idea behind their approach was to assign two different weights (1 or 0.5) for shrinkage parameters via preliminary tests [22][23][24][25][26]. While this approach has sound statistical performance in sparse and high-dimensional models, no software packages were implemented for the generalized ridge estimator.…”

Section: Introductionmentioning

confidence: 99%

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

Emura,

Matsumoto,

Uozumi

et al. 2024

Symmetry

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Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested that involved generalizing the uniform shrinkage of ridge regression to non-uniform shrinkage; this was shown to perform well in sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (first version published on 7 December 2023) that implements both the ridge estimator and generalized ridge estimator. The package is equipped with generalized cross-validation for the automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on the R package “glmnet”. Hence the generalized ridge estimator may be the most recommended estimator for sparse and high-dimensional models. We demonstrate the package using intracerebral hemorrhage data.

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Section: Introductionmentioning

confidence: 99%

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

Emura,

Matsumoto,

Uozumi

et al. 2024

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

g.ridge: An R Package for the Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

Emura,

Matsumoto,

Uozumi

et al. 2024

Preprint

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Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested by generalizing the uniform shrinkage of ridge regression to the non-uniform shrinkage, which was shown to perform well under sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (the first version published on 2023-12-07 at https://cran.r-project.org/package=g.ridge) that implements both the ridge estimator and generalized ridge estimators. The package equips with the generalized cross-validation for automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with the normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on “glmnet”, and hence, it can be the most recommended estimator under sparse and high-dimensional models. We demonstrate the package using the intracerebral hemorrhage data.

show abstract

Confidence interval for normal means in meta-analysis based on a pretest estimator

Cited by 2 publications

References 55 publications

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

g.ridge: An R Package for the Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

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