Admissibility and minimaxity of Bayes estimators for a normal mean matrix
In some invariant estimation problems under a group, the Bayes estimator against an invariant prior has equivariance as well. This is useful notably for evaluating the frequentist risk of the Bayes estimator.
Tsukuma, Hisayuki
core
We consider the problem of estimation of the parameters in Generalized Linear Models (GLM) with binary data when it is suspected that the parameter vector obeys some exact linear restrictions which are linearly independent with some degree of uncertainty.
Menéndez, M.L., Pardo, L., Pardo, M.C.
core
A new kind of stochastic restricted biased estimator for logistic regression model. [PDF]
Alheety MI, Månsson K, Golam Kibria BM.
europepmc +1 more source
On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in the large-sample limit. The asymptotic distributions are derived for both the case where the estimators are tuned to perform consistent model selection ...
Leeb, Hannes, Pötscher, Benedikt M.
core
SURPRISES IN HIGH-DIMENSIONAL RIDGELESS LEAST SQUARES INTERPOLATION. [PDF]
Hastie T +3 more
europepmc +1 more source
Variable selection in finite mixture of regression models using the skew-normal distribution. [PDF]
Yin J, Wu L, Dai L.
europepmc +1 more source
Who determines United States Healthcare out-of-pocket costs? Factor ranking and selection using ensemble learning. [PDF]
Zhang C, Ding Y, Peng Q.
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Lasso Monte Carlo, a variation on multi fidelity methods for high-dimensional uncertainty quantification. [PDF]
Albà A +3 more
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Shrinkage estimation of non-negative mean vector with unknown covariance under balance loss. [PDF]
Karamikabir H, Afshari M, Arashi M.
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Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection. [PDF]
Norouzirad M +3 more
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