Description Post-estimation shrinkage of regression coefficients in statistical modeling can be used to correct for the overestimation of regression coefficients caused by variable selection. While global shrinkage modifies all regression coefficients by the same factor, parameterwise shrinkage factors differ between regression coefficients. With highly correlated or semantically related variables, such as several columns of a design matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable and a compromise between global and parameterwise shrinkage, termed 'joint shrinkage', is a useful extension. A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA residuals i...
partially shrunk estimators, softly shrunk estimators, softly shrunk rank-reduced estimators,
Summary. The family of inverse regression estimators recently proposed by Cook and Ni (2005) have pr...
In high-dimensional factor models, both the factor loadings and the number of factors may change ove...
Description Post-estimation shrinkage of regression coefficients in statistical modeling can be used...
The predictive value of a statistical model can often be improved by applying shrinkage methods. Thi...
Regression analysis is a commonly used approach to modelling the relationships between dependent and...
[[abstract]]Estimation of regression coefficients in a linear regression model is essential not only...
10.1198/jasa.2009.0138Journal of the American Statistical Association104486747-75
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when t...
This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal d...
The paper discusses the merits of partial shrinkage of the ordinary least square estimator of the co...
Updating of the Gaussian graphical model via shrinkage estimation is studied. This shrinkage is towa...
We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage...
Overview of parameters in the final model, with estimated value, uncertainty, inter-individual varia...
International audiencePURPOSE: When information is sparse, individual parameters derived from a non-...
partially shrunk estimators, softly shrunk estimators, softly shrunk rank-reduced estimators,
Summary. The family of inverse regression estimators recently proposed by Cook and Ni (2005) have pr...
In high-dimensional factor models, both the factor loadings and the number of factors may change ove...
Description Post-estimation shrinkage of regression coefficients in statistical modeling can be used...
The predictive value of a statistical model can often be improved by applying shrinkage methods. Thi...
Regression analysis is a commonly used approach to modelling the relationships between dependent and...
[[abstract]]Estimation of regression coefficients in a linear regression model is essential not only...
10.1198/jasa.2009.0138Journal of the American Statistical Association104486747-75
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when t...
This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal d...
The paper discusses the merits of partial shrinkage of the ordinary least square estimator of the co...
Updating of the Gaussian graphical model via shrinkage estimation is studied. This shrinkage is towa...
We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage...
Overview of parameters in the final model, with estimated value, uncertainty, inter-individual varia...
International audiencePURPOSE: When information is sparse, individual parameters derived from a non-...
partially shrunk estimators, softly shrunk estimators, softly shrunk rank-reduced estimators,
Summary. The family of inverse regression estimators recently proposed by Cook and Ni (2005) have pr...
In high-dimensional factor models, both the factor loadings and the number of factors may change ove...