Some Insights About the Small Ball Probability Factorization for Hilbert Random Elements

TL;DR

This paper rigorously establishes asymptotic factorizations for the small-ball probability of Hilbert-valued random elements, linking it to principal components and volume, with practical estimation methods and simulations.

Contribution

It introduces new asymptotic factorizations for small-ball probabilities in Hilbert spaces and develops a non-parametric estimator for the surrogate intensity.

Findings

Asymptotic factorization relates SmBP to joint density of PCs and volume.

Estimator for surrogate intensity converges at the same rate using estimated PCs.

Simulations validate the theoretical results.

Abstract

Asymptotic factorizations for the small-ball probability (SmBP) of a Hilbert valued random element $X$ are rigorously established and discussed. In particular, given the first $d$ principal components (PCs) and as the radius $\varepsilon$ of the ball tends to zero, the SmBP is asymptotically proportional to (a) the joint density of the first $d$ PCs, (b) the volume of the $d$-dimensional ball with radius $\varepsilon$, and (c) a correction factor weighting the use of a truncated version of the process expansion. Moreover, under suitable assumptions on the spectrum of the covariance operator of $X$ and as $d$ diverges to infinity when $\varepsilon$ vanishes, some simplifications occur. In particular, the SmBP factorizes asymptotically as the product of the joint density of the first $d$ PCs and a pure volume parameter. All the provided factorizations allow to define a surrogate intensity of the SmBP that, in some cases, leads to a genuine intensity. To operationalize the stated results, a non-parametric estimator for the surrogate intensity is introduced and it is proved that the use of estimated PCs, instead of the true ones, does not affect the rate of convergence. Finally, as an illustration, simulations in controlled frameworks are provided.