SearcharxivSearch

arXiv · 1911.06444

Bounds to the Normal Approximation for Linear Recursions with Two Effects

Abstract

Let $X_0$ be a non-constant random variable with finite variance. Given an integer $k\ge2$, define a sequence $\{X_n\}_{n=1}^\infty$ of approximately linear recursions with small perturbations $\{\Delta_n\}_{n=0}^\infty$ by $$X_{n+1} = \sum_{i=1}^k a_{n,i} X_{n,i} + \Delta_n \quad \text{for all } n\ge0$$ where $X_{n,1},\dots,X_{n,k}$ are independent copies of the $X_n$ and $a_{n,1},\dots,a_{n,k}$ are real numbers. In 2004, Goldstein obtained bounds on the Wasserstein distance between the standard normal distribution and the law of $X_n$ which is in the form $C \gamma^n$ for some constants $C>0$ and $0 < \gamma < 1$. In this article, we extend the results to the case of two effects by studying a linear model $Z_n=X_n+Y_n$ for all $n\ge0$, where $\{Y_n\}_{n=1}^\infty$ is a sequence of approximately linear recursions with an initial random variable $Y_0$ and perturbations $\{\Lambda_n\}_{n=0}^\infty$, i.e., for some $\ell \ge2$, $$Y_{n+1} = \sum_{j=1}^\ell b_{n,j} Y_{n,j} + \Lambda_n \quad \text{for all } n\ge0$$ where $Y_n$ and $Y_{n,1},\dots,Y_{n,\ell}$ are independent and identically distributed random variables and $b_{n,1},\dots,b_{n,\ell}$ are real numbers. Applying the zero bias transformation in the Stein\rq s equation, we also obtain the bound for $Z_n$. Adding further conditions that the two models $(X_n,\Delta_n)$ and $(Y_n,\Lambda_n)$ are independent and that the difference between variance of $X_n$ and $Y_n$ is smaller than the sum of variances of their perturbation parts, our result is the same as previous work.

Explore related subjects

Keep this discovery

BibTeXRIS

Mongkhon Tuntapthai. 2019-11-15. Bounds to the Normal Approximation for Linear Recursions with Two Effects. https://arxiv.org/abs/1911.06444

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

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR