Statistica Sinica 27 (2017), 1949-1965
Abstract: Asymptotic factorizations for the small-ball probability (SmBP) of a Hilbert-valued random element Χ are established and discussed. In particular, given the first 𝒹 principal components (PCs) and as the radius ϵ 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 ϵ , and (c) a correction factor weighting the use of a truncated version of the process expansion. Under suitable assumptions on the spectrum of the covariance operator of Χ and as 𝒹 diverges to infinity when ϵ 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. The factorizations allow one 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.
Key words and phrases: Hilbert functional data, Karhunen–Loève decomposition,kernel density estimate, small ball probability.