sloe: Estimate the corrupted signal strength in a model with...

In brglm2: Bias Reduction in Generalized Linear Models

Estimate the corrupted signal strength in a model with (sub-)Gaussian covariates

Description

Estimate the corrupted signal strength in a model with (sub-)Gaussian covariates

Usage

sloe(object)

Arguments

object

an "mdyplFit" object.

Details

The Signal Strength Leave-One-Out Estimator (SLOE) is defined in Yadlowsky et al. (2021) when the model is estimated using maximum likelihood (i.e. when object$alpha = 1; see mdyplControl() for what alpha is). The SLOE adaptation when estimation is through maximum Diaconis-Ylvisaker prior penalized likelihood (mdypl_fit()) has been put forward in Sterzinger & Kosmidis (2025).

In particular, sloe() computes an estimate of the corrupted signal strength which is the limit

\nu^2

of var(X \hat\beta(\alpha)), where \hat\beta(\alpha) is the maximum Diaconis-Ylvisaker prior penalized likelihood (MDYPL) estimator as computed by mdyplFit() with shrinkage parameter alpha.

Value

A scalar.

Author(s)

Ioannis Kosmidis ⁠[aut, cre]⁠ [e-mail]

References

Sterzinger P, Kosmidis I (2024). Diaconis-Ylvisaker prior penalized likelihood for p/n \to \kappa \in (0,1) logistic regression. arXiv:2311.07419v2, https://arxiv.org/abs/2311.07419.

Yadlowsky S, Yun T, McLean C Y, D' Amour A (2021). SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. In M Ranzato, A Beygelzimer, Y Dauphin, P Liang, J W Vaughan (eds.), Advances in Neural Information Processing Systems, 34, 29517–29528. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2021/file/f6c2a0c4b566bc99d596e58638e342b0-Paper.pdf.

See Also

summary.mdyplFit()

brglm2 documentation built on Aug. 29, 2025, 5:25 p.m.