A Strong Law of Large Numbers under Sublinear Expectations

TL;DR

This paper establishes a strong law of large numbers under sublinear expectations, showing convergence of empirical averages to any value within a specified interval and characterizing tail triviality.

Contribution

It provides a new proof of the law of large numbers under sublinear expectations and extends results to Polish spaces and product spaces with tail triviality characterization.

Findings

Empirical averages cluster within [μ̲, μ̄] under each probability measure.

Existence of probability measures ensuring almost sure convergence to any μ in [μ̲, μ̄].

Characterization of tail σ-algebra triviality under sublinear expectations.

Abstract

We consider a sequence of i.i.d. random variables $\{\xi_k\}$under a sublinear expectation $\mathbb{E}=\sup_{P\in\Theta}E_P$. We first give a new proof to the fact that, under each $P\in\Theta$, any cluster point of the empirical averages $\bar{\xi}_n=(\xi_1+\cdots+\xi_n)/n$ lies in $[\underline{\mu}, \bar{\mu}]$ with $\underline{\mu}=-\mathbb{E}[-\xi_1], \bar{\mu}=\mathbb{E}[\xi_1]$. Then, we consider sublinear expectations on a Polish space $\Omega$, and show that for each constant $\mu\in [\underline{\mu},\bar{\mu}]$, there exists a probability $P_{\mu}\in\Theta$ such that \begin {eqnarray}\label {intro-a.s.} \lim_{n\rightarrow\infty}\bar{\xi}_n=\mu, \ P_{\mu}\textmd{-a.s.}, \end {eqnarray} supposing that $\Theta$ is weakly compact and $\{\xi_n\}\in L^1_{\mathbb{E}}(\Omega)$. Under the same conditions, we can get a generalization of (\ref {intro-a.s.}) in the product space $\Omega=\mathbb{R}^{\mathbb{N}}$ with $\mu\in [\underline{\mu},\bar{\mu}]$ replaced by $\Pi=\pi(\xi_1, \cdots,\xi_d)\in [\underline{\mu},\bar{\mu}]$, where $\pi$ is a Borel measurable function on $\mathbb{R}^d$, $d\in\mathbb{R}$. Finally, we characterize the triviality of the tail $\sigma$-algebra of i.i.d. random variables under a sublinear expectation.