Optimal cross-validation in density estimation with the $L^2$-loss

TL;DR

This paper provides a detailed analysis of cross-validation methods for density estimation using the $L^2$-loss, establishing optimality results and practical improvements over traditional approaches.

Contribution

It introduces closed-form expressions for leave-$p$-out CV risk estimators, demonstrating their advantages and proving the optimality of leave-one-out CV for risk estimation.

Findings

Closed-form expressions improve variability and computational efficiency.

Leave-one-out CV is optimal for risk estimation.

Model selection consistency depends on the ratio p/n and the estimator's convergence rate.

Abstract

We analyze the performance of cross-validation (CV) in the density estimation framework with two purposes: (i) risk estimation and (ii) model selection. The main focus is given to the so-called leave-$p$-out CV procedure (Lpo), where $p$ denotes the cardinality of the test set. Closed-form expressions are settled for the Lpo estimator of the risk of projection estimators. These expressions provide a great improvement upon $V$-fold cross-validation in terms of variability and computational complexity. From a theoretical point of view, closed-form expressions also enable to study the Lpo performance in terms of risk estimation. The optimality of leave-one-out (Loo), that is Lpo with $p=1$, is proved among CV procedures used for risk estimation. Two model selection frameworks are also considered: estimation, as opposed to identification. For estimation with finite sample size $n$, optimality is achieved for $p$ large enough [with $p/n=o(1)$] to balance the overfitting resulting from the structure of the model collection. For identification, model selection consistency is settled for Lpo as long as $p/n$ is conveniently related to the rate of convergence of the best estimator in the collection: (i) $p/n\to1$ as $n\to+\infty$ with a parametric rate, and (ii) $p/n=o(1)$ with some nonparametric estimators. These theoretical results are validated by simulation experiments.