Search arXivSearch

arXiv · 2002.09427

Central Limit Theorems for Markov Chains from Wasserstein Convergence Rates

Abstract

We give sufficient conditions for central limit theorems (CLTs) for additive functionals of Markov chains in terms of quantitative Wasserstein convergence rates, including suitable subgeometric rates. For a given metric $ψ$, we establish CLTs for $ψ$-Lipschitz functions under moment conditions by showing that suitable $1$-Wasserstein convergence rates imply either the Maxwell--Woodroofe projective criterion or convergence of the associated Poisson series. We then extend this framework beyond the $ψ$-Lipschitz setting in two directions. First, by reweighting $ψ$ with a non-negative function $V$, we construct a weighted path metric under which functions with $V$-controlled increments are Lipschitz. We derive convergence bounds in the Wasserstein distance induced by this new metric from corresponding bounds in the Wasserstein distance induced by $ψ$, thereby obtaining CLTs for this broad class of functions. Second, we consider functions admitting integrable increment envelopes and derive CLTs from quantitative $2$-Wasserstein convergence rates. On $\mathbb R^d$, pointwise Sobolev inequalities and polynomial bounds on the gradient provide concrete sufficient conditions for constructing such envelopes. We illustrate the results with nonlinear autoregressive processes and a random walk on the one-dimensional torus exhibiting subgeometric Wasserstein convergence.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rui Jin. 2026-09-04. Central Limit Theorems for Markov Chains from Wasserstein Convergence Rates. https://arxiv.org/abs/2002.09427

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Sample complexity and weak limits of nonsmooth multimarginal Schrödinger system with application to optimal transport barycenter

Multimarginal optimal transport (MOT) has emerged as a useful framework for many applied problems. However, compared to the well-studied classical two-marginal optimal transport theory, analysis of MOT is far more challenging and remains much less developed. In this paper, we study the statistical estimation and inference problems for the entropic MOT (EMOT), whose optimal solution is characterized by the multimarginal Schrödinger system. Assuming only boundedness of the cost function, we derive sharp sample complexity for estimating several key quantities pertaining to EMOT (cost functional and Schrödinger coupling) from point clouds that are randomly sampled from the input marginal distributions. Moreover, with substantially weaker smoothness assumption on the cost function than the existing literature, we derive distributional limits and bootstrap validity of various key EMOT objects. As an application, we propose the multimarginal Schrödinger barycenter as a new and natural way to regularize the exact Wasserstein barycenter and demonstrate its statistical optimality.

math.ST

Nonparametric spectral density estimation using interactive mechanisms under local differential privacy

We study the problem of estimating the spectral density of a centered stationary Gaussian time series under local differential privacy constraints. Specifically, we propose new interactive privacy mechanisms for three tasks: recovering a single covariance coefficient, recovering the spectral density at a fixed frequency, and global recovery. Our approach achieves faster rates through a two-stage process: we first apply the Laplace mechanism to the truncated value, and then use the resulting privatized sample to learn about the dependence mechanism in the time series. For spectral densities belonging to Hölder and Sobolev smoothness classes, we demonstrate that our algorithms improve upon the non-interactive mechanism of Kroll (2024) for small privacy parameter $α$, since the pointwise rates depend on $nα^2$ instead of $nα^4$. Moreover, we show that the rate $(nα^4)^{-1}$ is optimal for estimating a covariance coefficient with non-interactive mechanisms. However, the $L_2$ rate of our interactive estimator is slower than the pointwise rate. We show how to use these procedures to provide a bona fide locally differentially private estimator of the entire covariance matrix. A simulation study validates our findings.

math.ST

Estimating eigenvectors and eigenspaces of covariance matrices: Optimal Bounds and Conditions for Consistency

Let $X = [ ξ_1, \,\, ξ_2,...\,\, ,ξ_d]^\top$ be a zero-mean random vector of large dimension $d$ ($d \rightarrow \infty$) with (hidden) covariance matrix $M = (m_{ij})_{1 \leq i, j \leq d},$ where $m_{ij} = m_{ji} = \textbf{Cov}(ξ_i, ξ_j).$ Let $X_1, X_2, \dots, X_n$ be $n$ iid samples of $X$. Consider the sample covariance matrix $$\textstyle \tilde{M} := \frac{1}{n} \sum_{i=1}^{n} X_i X_i^\top.$$ In practice, one frequently uses the eigenvectors and eigenspaces of $\tilde M$ as estimators for those of $M$. A central task is to provide an error analysis for these estimators. In this paper, we provide an optimal error analysis, obtaining upper and lower bounds of matching order of magnitude, for a wide range of parameters $d$ and $n$, under mild assumptions on $M$. As corollaries, we obtain new necessary and sufficient conditions for the consistency of the estimators. In these conditions, we only require the number of samples $n$ to depend linearly on the effective rank of $M$, which can be much smaller than the dimension $d$.

math.ST