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In this paper, we resolve a longstanding open statistical problem. The problem is to mathematically confirm Yule's 1926 empirical finding of "nonsense correlation" (\cite{Yule}). We do so by analytically determining the second moment of the…

Statistics Theory · Mathematics 2016-08-31 Philip Ernst , Larry Shepp , Abraham Wyner

In 1926, G. Udny Yule considered the following: given a sequence of pairs of random variables $\{X_k,Y_k \}$ ($k=1,2, \ldots, n$), and letting $X_i = S_i$ and $Y_ i= S'_i$ where $S_i$ and $S'_i$ are the partial sums of two independent…

Statistics Theory · Mathematics 2023-01-31 Philip A. Ernst , L. C. G. Rogers , Quan Zhou

This paper begins with a study of the exact distribution of the empirical correlation of two independent AR(1) processes with Gaussian increments. We proceed to develop rates of convergence for the distribution of the scaled empirical…

Statistics Theory · Mathematics 2025-08-12 Philip A. Ernst , Dongzhou Huang

We study the continuous-time version of the empirical correlation coefficient between the paths of two possibly correlated Ornstein-Uhlenbeck processes, known as Yule's nonsense correlation for these paths. Using sharp tools from the…

Probability · Mathematics 2025-04-25 Soukaina Douissi , Philip Ernst , Frederi Viens

We study the Wasserstein distance $W_2$ for Gaussian samples. We establish the exact rate of convergence $\sqrt{\log\log n/n}$ of the expected value of the $W_2$ distance between the empirical and true $c.d.f.$'s for the normal…

Probability · Mathematics 2020-01-28 Philippe Berthet , Jean-Claude Fort

Fix an irrational number $\alpha$. Let $X_1,X_2,\cdots$ be independent, identically distributed, integer-valued random variables with characteristic function $\varphi$, and let $S_n=\sum_{i=1}^n X_i$ be the partial sums. Consider the random…

Probability · Mathematics 2024-11-26 Bingyao Wu , Jie-Xiang Zhu

We construct a coupling between the random walk composed of L\'evy area increments from a $d$-dimensional Brownian motion and a random walk composed of quadratic polynomials of Gaussian random variables. This coupling construction is used…

Probability · Mathematics 2016-05-31 Guy Flint

In this paper, we establish sharp upper and lower bounds on the convergence rate of the empirical measures of point processes under the Wasserstein distance. To this end, we first introduce a new metric on the space of counting measures…

Statistics Theory · Mathematics 2026-04-28 Dongzhou Huang , Tianyi Jiang , Haonan Wang

We study the behavior of the random walk in a continuum independent long-range percolation model, in which two given vertices $x$ and $y$ are connected with probability that asymptotically behaves like $|x-y|^{-\alpha}$ with $\alpha>d$,…

Probability · Mathematics 2022-09-30 Ercan Sönmez , Arnaud Rousselle

Chen [Ann. Appl. Probab. {\bf 11} (2001), 1242--1262] derived exact convergence rates in a central limit theorem and a local limit theorem for a supercritical branching Wiener process.We extend Chen's results to a branching random walk…

Probability · Mathematics 2015-11-17 Zhiqiang Gao , Quansheng Liu

Estimating the rate of convergence of the empirical measure of an i.i.d. sample to the reference measure is a classical problem in probability theory. Extending recent results of Ambrosio, Stra and Trevisan on 2-dimensional manifolds, in…

Probability · Mathematics 2024-02-20 Bence Borda

These notes were written for the mini-course "Extrema of log-correlated random variables: Principles and Examples" at the Introductory School held in January 2015 at the Centre International de Rencontres Math\'ematiques in Marseille. There…

Probability · Mathematics 2016-01-05 Louis-Pierre Arguin

We construct a continuous-time non-commutative random walk on $U(\mathfrak{gl}_N)$ with dilation maps $U(\mathfrak{gl}_N)\rightarrow L^2(U(N))^{\otimes\infty}$. This is an analog of a continuous-time non-commutative random walk on the group…

Representation Theory · Mathematics 2016-12-20 Jeffrey Kuan

The purpose of this paper is to analyze the distribution distance between random vectors derived from the magnitude of the analytic wavelet transform of the squared envelopes of Gaussian processes and their large-scale limits. When the…

Probability · Mathematics 2024-09-05 Gi-Ren Liu

The Wasserstein distance between two probability measures on a metric space is a measure of closeness with applications in statistics, probability, and machine learning. In this work, we consider the fundamental question of how quickly the…

Probability · Mathematics 2017-07-04 Jonathan Weed , Francis Bach

This paper deals with the problem of quantifying the approximation a probability measure by means of an empirical (in a wide sense) random probability measure, depending on the first n terms of a sequence of random elements. In Section 2,…

Probability · Mathematics 2018-08-23 Emanuele Dolera , Eugenio Regazzini

We calculate the two-point correlation function <x(t2)x(t1)> for a subdiffusive continuous time random walk in a parabolic potential, generalizing well-known results for the single-time statistics to two times. A closed analytical…

Statistical Mechanics · Physics 2009-11-13 A. Baule , R. Friedrich

In this paper, we study the distribution of the so-called "Yule's nonsense correlation statistic" on a time interval $[0,T]$ for a time horizon $T>0$ , when $T$ is large, for a pair $(X_{1},X_{2})$ of independent Ornstein-Uhlenbeck…

Probability · Mathematics 2021-08-09 Soukaina Douissi , Frederi G. Viens , Khalifa Es-Sebaiy

The autocovariance and cross-covariance functions naturally appear in many time series procedures (e.g., autoregression or prediction). Under assumptions, empirical versions of the autocovariance and cross-covariance are asymptotically…

Statistics Theory · Mathematics 2023-05-09 Andreas Anastasiou , Tobias Kley

We consider point process convergence for sequences of iid random walks. The objective is to derive asymptotic theory for the largest extremes of these random walks. We show convergence of the maximum random walk to the Gumbel or the…

Probability · Mathematics 2020-11-10 Thomas Mikosch , Jorge Yslas
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