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Found 4 papers, 2 papers with code
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions
We study the problem of out-of-sample risk estimation in the high dimensional regime where both the sample size $n$ and number of features $p$ are large, and $n/p$ can be less than one.
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression
This paper studies the problem of risk estimation under the moderately high-dimensional asymptotic setting $n, p \rightarrow \infty$ and $n/p \rightarrow \delta>1$ ($\delta$ is a fixed number), and proves the consistency of three risk estimates that have been successful in numerical studies, i. e., leave-one-out cross validation (LOOCV), approximate leave-one-out (ALO), and approximate message passing (AMP)-based techniques.
A scalable estimate of the extra-sample prediction error via approximate leave-one-out
Motivated by the low bias of the leave-one-out cross validation (LO) method, we propose a computationally efficient closed-form approximate leave-one-out formula (ALO) for a large class of regularized estimators.
Methodology
Robust and scalable Bayesian analysis of spatial neural tuning function data
On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons.
