Optimal Inference with a Multidimensional Multiscale Statistic

TL;DR

This paper introduces a multiscale statistical method for optimal inference in high-dimensional stochastic processes, extending previous one-dimensional work to multiple dimensions and applying it to hypothesis testing.

Contribution

It develops a multidimensional multiscale statistic, proves its finiteness, and constructs optimal tests for function nullity and specific alternatives, generalizing prior one-dimensional results.

Findings

Proposed a multiscale statistic for $d$-dimensional processes.

Proved the almost sure finiteness of the statistic.

Constructed optimal hypothesis tests for various function classes.

Abstract

We observe a stochastic process $Y$ on $[0,1]^d$ ($d\geq 1$) satisfying $dY(t)=n^{1/2}f(t)dt$ + $dW(t)$, $t \in [0,1]^d$, where $n \geq 1$ is a given scale parameter (`sample size'), $W$ is the standard Brownian sheet on $[0,1]^d$ and $f \in L_1([0,1]^d)$ is the unknown function of interest. We propose a multivariate multiscale statistic in this setting and prove its almost sure finiteness; this extends the work of D\"umbgen and Spokoiny (2001) who proposed the analogous statistic for $d=1$. We use the proposed multiscale statistic to construct optimal tests for testing $f=0$ versus (i) appropriate H\"{o}lder classes of functions, and (ii) alternatives of the form $f=\mu_n \mathbb{I}_{B_n}$, where $B_n$ is an axis-aligned hyperrectangle in $[0,1]^d$ and $\mu_n \in \mathbb{R}$; $\mu_n$ and $B_n$ unknown. In the process we generalize Theorem 6.1 of D\"umbgen and Spokoiny (2001) about stochastic processes with sub-Gaussian increments on a pseudometric space, which is of independent interest.