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Fangjun Xu

Publications and source records attributed to Fangjun Xu.

At least 19 recordsLinked to original sources
arXiv2025

Limit theorems for functionals of linear processes in critical regions

Let $X=\{X_n: n\in\mathbb{N}\}$ be the linear process defined by $X_n=\sum^{\infty}_{j=1} a_j\varepsilon_{n-j}$, where the coefficients $a_j=j^{-\beta}\ell(j)$ are constants with $\beta>0$ and $\ell$ a slowly varying function, and the innovations $\{\varepsilon_n\}_{n\in\mathbb{Z}}$ are i.i.d. random variables belonging to the domain of attraction of an $\alpha$-stable law with $\alpha\in(0,2]$. Limit theorems for the partial sum $ S_{[Nt]}=\sum^{[Nt]}_{n=1}[K(X_n)-\mathbb{E}K(X_n)]$ with proper measurable functions $K$ have been extensively studied, except for two critical regions: I. $\alpha\in(1,2),\beta=1$ and II. $\alpha\beta=2,\beta\geq1$. In this paper, we address these open scenarios and identify the asymptotic distributions of $S_{[Nt]}$ under mild conditions.

math.PR↗
arXiv2024

Kernel entropy estimation for linear processes II

Let $X=\{X_n: n\in \mathbb{N}\}$ be a linear process with bounded probability density function $f(x)$. Under certain conditions, we use the kernel estimator \[ \frac{2}{n(n-1)h_n} \sum_{1\le i<j\le n}K\Big(\frac{X_i-X_j}{h_n}\Big) \] to estimate the quadratic functional of $\int_{\mathbb{R}}f^2(x)dx$ of the linear process $X=\{X_n: n\in \mathbb{N}\}$ and improve the corresponding results in [4].

math.ST↗
arXiv2023

A limit theorem for some linear processes with innovations in the domain of attraction of a stable law

Let $X=\{X_n: n\in\mathbb{N}\}$ be a linear process in which the coefficients are of the form $a_i=i^{-1}\ell(i)$ with $\ell$ being a slowly varying function at the infinity and the innovations are independent and identically distributed random variables belonging to the domain of attraction of an $\alpha$-stable law with $\alpha\in (1, 2]$. We will establish the asymptotic behavior of the partial sum process \[ \bigg\{\sum\limits_{n=1}^{[Nt]} X_n: t\geq 0\bigg\} \] as $N$ tends to infinity, where $[t]$ is the integer part of the non-negative number $t$.

math.PR↗
arXiv2023

Limit theorems for additive functionals of some self-similar Gaussian processes

Under certain mild conditions, limit theorems for additive functionals of some $d$-dimensional self-similar Gaussian processes are obtained. These limit theorems work for general Gaussian processes including fractional Brownian motions, sub-fractional Brownian motions and bi-fractional Brownian motions. To prove these results, we use the method of moments and an enhanced chaining argument. The Gaussian processes under consideration are required to satisfy certain strong local nondeterminism property. A tractable sufficient condition for the strong local nondeterminism property is given and it only relays on the covariance functions of the Gaussian processes. Moreover, we give a sufficient condition for the distribution function of a random vector to be determined by its moments.

math.PR↗
arXiv2023

Limit theorems for functionals of long memory linear processes with infinite variance

Let $X=\{X_n: n\in\mathbb{N}\}$ be a long memory linear process in which the coefficients are regularly varying and innovations are independent and identically distributed and belong to the domain of attraction of an $\alpha$-stable law with $\alpha\in (0, 2)$. Then, for any integrable and square integrable function $K$ on $\mathbb{R}$, under certain mild conditions, we establish the asymptotic behavior of the partial sum process \[ \left\{\sum\limits_{n=1}^{[Nt]}\big[K(X_n)-\E K(X_n)\big]:\; t\geq 0\right\} \] as $N$ tends to infinity, where $[Nt]$ is the integer part of $Nt$ for $t\geq 0$.

math.PR↗
arXiv2022

Kernel entropy estimation for long memory linear processes with infinite variance

Let $X=\{X_n: n\in\mathbb{N}\}$ be a long memory linear process with innovations in the domain of attraction of an $\alpha$-stable law $(0<\alpha<2)$. Assume that the linear process $X$ has a bounded probability density function $f(x)$. Then, under certain conditions, we consider the estimation of the quadratic functional $\int_{\mathbb{R}} f^2(x) \,dx$ by using the kernel estimator \[ T_n(h_n)=\frac{2}{n(n-1)h_n}\sum_{1\leq j<i\leq n}K\left(\frac{X_i-X_j}{h_n}\right). \] The simulation study for long memory linear processes with symmetric $\alpha$-stable innovations is also given.

math.ST↗
arXiv2020

Derivatives of local times for some Gaussian fields II

Given a $(2,d)$-Gaussian field \[ Z=\big\{ Z(t,s)= X^{H_1}_t -\tilde{X}^{H_2}_s, s,t \ge 0\big\}, \] where $X^{H_1}$ and $\tilde{X}^{H_2}$ are independent $d$-dimensional centered Gaussian processes satisfying certain properties, we will give the necessary condition for existence of derivatives of the local time of $Z$.

math.PR↗
arXiv2019

Derivatives of local times for some Gaussian fields

In this article, we consider derivatives of local time for a $(2,d)$-Gaussian field \[ Z=\big\{ Z(t,s)= X^{H_1}_t -\widetilde{X}^{H_2}_s, s,t \ge 0\big\}, \] where $X^{H_1}$ and $\widetilde{X}^{H_2}$ are two independent processes from a class of $d$-dimensional centered Gaussian processes satisfying certain local nondeterminism property. We first give a condition for existence of derivatives of the local time. Then, under this condition, we show that derivatives of the local time are Hölder continuous in both time and space variables. Moreover, under some additional assumptions, we show that this condition is also necessary for existence of derivatives of the local time at the origin.

math.PR↗
arXiv2019

Fractional stochastic wave equation driven by a Gaussian noise rough in space

In this article, we consider fractional stochastic wave equations on $\mathbb R$ driven by a multiplicative Gaussian noise which is white/colored in time and has the covariance of a fractional Brownian motion with Hurst parameter $H\in(\frac14, \frac12)$ in space. We prove the existence and uniqueness of the mild Skorohod solution, establish lower and upper bounds for the $p$-th moment of the solution for all $p\ge2$, and obtain the Hölder continuity in time and space variables for the solution.

math.PR↗
arXiv2018

Limit theorems for functionals of two independent Gaussian processes

Under certain mild conditions, some limit theorems for functionals of two independent Gaussian processes are obtained. The results apply to general Gaussian processes including fractional Brownian motion, sub-fractional Brownian motion and bi-fractional Brownian motion. A new and interesting phenomenon is that, in comparison with the results for fractional Brownian motion, extra randomness appears in the limiting distributions for Gaussian processes with nonstationary increments, say sub-fractional Brownian motion and bi-fractional Brownian. The results are obtained based on the method of moments, in which Fourier analysis, the chaining argument introduced in \cite{nx1} and a paring technique are employed.

math.PR↗
arXiv2017

Kernel entropy estimation for linear processes

Let $\{X_n: n\in \mathbb{N}\}$ be a linear process with bounded probability density function $f(x)$. We study the estimation of the quadratic functional $\int_{\mathbb{R}} f^2(x)\, dx$. With a Fourier transform on the kernel function and the projection method, it is shown that, under certain mild conditions, the estimator \[ \frac{2}{n(n-1)h_n} \sum_{1\le i<j\le n}K\left(\frac{X_i-X_j}{h_n}\right) \] has similar asymptotical properties as the i.i.d. case studied in Giné and Nickl (2008) if the linear process $\{X_n: n\in \mathbb{N}\}$ has the defined short range dependence. We also provide an application to $L^2_2$ divergence and the extension to multivariate linear processes. The simulation study for linear processes with Gaussian and $α$-stable innovations confirms our theoretical results. As an illustration, we estimate the $L^2_2$ divergences among the density functions of average annual river flows for four rivers and obtain promising results.

math.ST↗
arXiv2015

Density convergence in the Breuer-Major theorem for Gaussian stationary sequences

Consider a Gaussian stationary sequence with unit variance $X=\{X_k;k\in {\mathbb{N}}\cup\{0\}\}$. Assume that the central limit theorem holds for a weighted sum of the form $V_n=n^{-1/2}\sum^{n-1}_{k=0}f(X_k)$, where $f$ designates a finite sum of Hermite polynomials. Then we prove that the uniform convergence of the density of $V_n$ towards the standard Gaussian density also holds true, under a mild additional assumption involving the causal representation of $X$.

math.PR↗