In this article we derive the needed asymptotic expansions only for the simplest possible case: one-dimensional linear regression where the regularization ...
Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, ...
Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, ...
We'll be working in what is known as a proportional asymptotics model, where the dimension d and num- ber of samples n diverge proportionally. That is, d n → ...
Mar 7, 2024 · An asymptotic theory is established for linear functionals of the predictive function given by kernel ridge regression, when the reproducing kernel Hilbert ...
Missing: Regularization: Dimensional
The algorithm uses multiplicative updates which are fast and simultaneous. Asymptotic results are also developed for the constrained penalized likelihood ...
Missing: Theory | Show results with:Theory
This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives.
We provide a unified analysis of the predictive risk of ridge regres- sion and regularized discriminant analysis in a dense random effects model.
Our asymptotic characterization enables us to derive optimal regularization sequences to either minimize the MSE or to maximize the power in variable selection ...
Mar 7, 2024 · An asymptotic theory is established for linear functionals of the predictive function given by kernel ridge regression, when the reproducing kernel Hilbert ...