Statistical Inference for Stochastic Processes最新文献

英文中文

Guided simulation of conditioned chemical reaction networks. 条件化学反应网络的引导模拟。

IF 0.7 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2025-01-01 Epub Date: 2025-05-17 DOI: 10.1007/s11203-025-09326-9

Marc Corstanje, Frank van der Meulen

Let X be a chemical reaction process, modeled as a multi-dimensional continuous-time jump process. Assume that at given times $0 < t_{1} < \dots < t_{n}$ , linear combinations $v_{i} = L_{i} X (t_{i}), i = 1, \dots, n$ are observed for given matrices $L_{i}$ . We show how the process that is conditioned on hitting the states $v_{1}, \dots, v_{n}$ is obtained by a change of measure on the law of the unconditioned process. This results in an algorithm for obtaining weighted samples from the conditioned process. Our results are illustrated by numerical simulations.

设X为一个化学反应过程，建模为一个多维连续时间跳跃过程。假设在给定的0 t 1⋯t n时刻，对于给定的矩阵L i，观察到线性组合v i = L i X (ti), i = 1,⋯n。我们展示了以达到状态v1，⋯v n为条件的过程是如何通过对无条件过程定律的测度变化而获得的。这就产生了一种从条件过程中获得加权样本的算法。数值模拟表明了我们的结果。

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引用次数: 0

Adaptive exact recovery in sparse nonparametric models. 稀疏非参数模型的自适应精确恢复。

IF 1 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2025-01-01 Epub Date: 2025-10-27 DOI: 10.1007/s11203-025-09333-w

Natalia Stepanova, Marie Turcicova

We observe an unknown function of d variables $f (t)$ , $t \in {[0, 1]}^{d}$ , in the Gaussian white noise model of intensity $ε > 0$ . We assume that the function f is regular and that it is a sum of k-variate functions, where k varies from 1 to s ( $1 \leq s \leq d$ ). These functions are unknown to us and only a few of them are nonzero. In this article, we address the problem of identifying the nonzero components of f in the case when $d = d_{ε} \to \infty$ as $ε \to 0$ and s is either fixed or $s = s_{ε} \to \infty$ , $s = o (d)$ as $ε \to \infty$ . This may be viewed as a variable selection problem. We derive the conditions when exact variable selection in the model at hand is possible and provide a selection procedure that achieves this type of selection. The procedure is adaptive to a degree of model sparsity described by the sparsity parameter $β \in (0, 1)$ . We also derive conditions that make the exact variable selection impossible. Our results augment previous work in this area.

在强度为ε > 0的高斯白噪声模型中，我们观察到d个变量f (t)的未知函数，t∈[0,1]d。我们假设函数f是正则函数，它是k变量函数的和，其中k从1到s变化（1≤s≤d）。这些函数是未知的，只有少数是非零的。在本文中，我们解决了当d = d ε→∞为ε→0且s是固定的或s = s ε→∞,s = o (d)为ε→∞时f的非零分量的辨识问题。这可以看作是一个变量选择问题。我们推导了在模型中可能进行精确变量选择的条件，并提供了实现这种选择的选择过程。该过程自适应于由稀疏度参数β∈(0,1)描述的模型稀疏度。我们还推导出使精确的变量选择不可能的条件。我们的结果加强了以前在这一领域的工作。

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引用次数: 0

The distribution of the maximum likelihood estimates of the change point and their relation to random walks 变化点最大似然估计值的分布及其与随机游走的关系

IF 0.8 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-12-19 DOI: 10.1007/s11203-023-09304-z

S. Fotopoulos

引用次数: 0

Weak convergence of the conditional U-statistics for locally stationary functional time series 局部静止函数时间序列条件 U 统计量的弱收敛性

IF 0.8 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-12-18 DOI: 10.1007/s11203-023-09305-y

Inass Soukarieh, S. Bouzebda

引用次数: 0

Nonparametric estimation for random effects models driven by fractional Brownian motion using Hermite polynomials 利用赫米特多项式对分数布朗运动驱动的随机效应模型进行非参数估计

IF 0.8 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-12-02 DOI: 10.1007/s11203-023-09302-1

Hamid El Maroufy, Souad Ichi, Mohamed El Omari, Yousri Slaoui

引用次数: 0

Asymptotic expansion of an estimator for the Hurst coefficient 赫斯特系数估计量的渐近展开式

Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-09-25 DOI: 10.1007/s11203-023-09298-8

Yuliya Mishura, Hayate Yamagishi, Nakahiro Yoshida

Abstract Asymptotic expansion is presented for an estimator of the Hurst coefficient of a fractional Brownian motion. We first derive the expansion formula of the principal term of the error of the estimator using a recently developed theory of asymptotic expansion of the distribution of Wiener functionals, and utilize the perturbation method on the obtained formula in order to calculate the expansion of the estimator. We also discuss some second-order modifications of the estimator. Numerical results show that the asymptotic expansion attains higher accuracy than the normal approximation.

摘要给出了分数阶布朗运动赫斯特系数估计量的渐近展开式。我们首先利用最近发展的Wiener泛函分布渐近展开理论，推导了估计量误差主项的展开式，并利用摄动法对得到的公式进行了估计量展开式的计算。我们还讨论了估计量的一些二阶修正。数值结果表明，渐近展开法比正态逼近法具有更高的精度。

引用次数: 0

Second-order robustness for time series inference 时间序列推理的二阶鲁棒性

Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-09-23 DOI: 10.1007/s11203-023-09296-w

Xiaofei Xu, Yan Liu, Masanobu Taniguchi

引用次数: 0

Localization of two radioactive sources on the plane 平面上两个放射源的定位

Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-09-19 DOI: 10.1007/s11203-023-09297-9

O Chernoyarov, S Dachian, C Farinetto, Yu Kutoyants

It is considered the problem of localization on the plane of two radioactive sources by K detectors. Each detector records a realization of inhomogeneous Poisson process and the intensity function of this process is a sum of a signal arriving from the sources and the constant Poisson noise of known intensity. The time of the beginning of emissions of two sources is known and the main problem is the estimation of the position of the sources. The properties of the MLE and Bayessian estimators are described in the asymptotics of large signals in three situations of different regularities of the fronts of the signals: smooth, cusp-type and change-point type.

研究了K探测器在两个放射源平面上的定位问题。每个探测器记录了一个非齐次泊松过程的实现，该过程的强度函数是来自源的信号与已知强度的恒定泊松噪声的和。两个源开始排放的时间是已知的，主要问题是对源位置的估计。讨论了大信号在平滑型、尖峰型和变点型三种不同信号前沿规律情况下的渐近性。

引用次数: 0

Inference in generalized exponential O–U processes with change-point 具有变点的广义指数O-U过程的推理

Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-09-01 DOI: 10.1007/s11203-023-09293-z

Yunhong Lyu, Sévérien Nkurunziza

引用次数: 0

A Cramér–von Mises test for a class of mean time dependent CHARN models with application to change-point detection 一类平均时间相关CHARN模型的cram<s:1> - von Mises检验及其在变点检测中的应用

IF 0.8 Q3 STATISTICS & PROBABILITY

Statistical Inference for Stochastic Processes

Pub Date : 2023-08-23 DOI: 10.1007/s11203-023-09295-x

J. Ngatchou-Wandji, Marwa Ltaifa

引用次数: 0

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