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The approximation of integral functionals with respect to a stationary Markov process by a Riemann-sum estimator is studied. Stationarity and the functional calculus of the infinitesimal generator of the process are used to get a better…

Probability · Mathematics 2016-10-18 Randolf Altmeyer , Jakub Chorowski

In this paper some general theory is presented for locally stationary processes based on the stationary approximation and the stationary derivative. Laws of large numbers, central limit theorems as well as deterministic and stochastic bias…

Statistics Theory · Mathematics 2017-11-21 Rainer Dahlhaus , Stefan Richter , Wei Biao Wu

We establish a central limit theorem and an invariance principle for stationary random fields, with projective-type conditions. Our result is obtained via an m-dependent approximation method. As applications, we establish invariance…

Probability · Mathematics 2012-04-12 Yizao Wang , Michael Woodroofe

We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a single trajectory of Markovian data. Our methodology has practical applications in various scenarios, such as Stochastic Gradient Descent (SGD)…

Statistics Theory · Mathematics 2023-02-21 Xiang Li , Jiadong Liang , Zhihua Zhang

This paper provides refined versions of some known functional central limit theorems for conditional Poisson sampling which are more suitable for applications. The theorems presented in this paper are generalizations of some results that…

Statistics Theory · Mathematics 2019-06-18 Leo Pasquazzi

Ordinary differential equations obtained as limits of Markov processes appear in many settings. They may arise by scaling large systems, or by averaging rapidly fluctuating systems, or in systems involving multiple time-scales, by a…

Probability · Mathematics 2014-03-24 Hye-Won Kang , Thomas G. Kurtz , Lea Popovic

In this paper we study the additive functionals of Markov chains via conditioning with respect to both past and future of the chain. We shall point out new sufficient projective conditions, which assure that the variance of partial sums of…

Probability · Mathematics 2020-05-19 Magda Peligrad

Whereas classical invariance principles for ergodic Markov chains address the situation in which the time horizon of observations is much larger than the mixing time, the quality of approximation is questionable when this is not the case…

Probability · Mathematics 2026-05-12 Gabriele Bellerino , Angelika Rohde

We obtain necessary and sufficient conditions for the regular variation of the variance of partial sums of functionals of discrete and continuous-time stationary Markov processes with normal transition operators. We also construct a class…

Probability · Mathematics 2014-05-13 George Deligiannidis , Magda Peligrad , Sergey Utev

We provide a framework for empirical process theory of locally stationary processes using the functional dependence measure. Our results extend known results for stationary Markov chains and mixing sequences by another common possibility to…

Statistics Theory · Mathematics 2021-08-20 Nathawut Phandoidaen , Stefan Richter

In this paper we study the almost sure central limit theorem started from a point for additive functionals of a stationary and ergodic Markov chain via a martingale approximation in the almost sure sense. As a consequence we derive the…

Probability · Mathematics 2009-11-26 Christophe Cuny , Magda Peligrad

In this paper, we derive asymptotic results for L^1-Wasserstein distance between the distribution function and the corresponding empirical distribution function of a stationary sequence. Next, we give some applications to dynamical systems…

Probability · Mathematics 2008-12-16 Sophie Dede

We investigate the convergence in distribution of sequential empirical processes of dependent data indexed by a class of functions F. Our technique is suitable for processes that satisfy a multiple mixing condition on a space of functions…

Probability · Mathematics 2014-09-26 Herold Dehling , Olivier Durieu , Marco Tusche

We develop a general framework for extracting highly uniform bounds on local stability for stochastic processes in terms of information on fluctuations or crossings. This includes a large class of martingales: As a corollary of our main…

Probability · Mathematics 2024-08-05 Morenikeji Neri , Thomas Powell

By using limit theorems of uniform mixing Markov processes and martingale difference sequences, the strong law of large numbers, central limit theorem, and the law of iterated logarithm are established for additive functionals of…

Probability · Mathematics 2019-04-08 Jianhai Bao , Feng-Yu Wang , Chenggui Yuan

We introduce a new basic model for independent and identical distributed sequence on the canonical space $(\mathbb{R}^\mathbb{N},\mathcal{B}(\mathbb{R}^\mathbb{N}))$ via probability kernels with model uncertainty. Thanks to the well-defined…

Probability · Mathematics 2022-03-02 Xinpeng Li

Through certain appropriate constructions, we establish periodic solutions in distribution for some stochastic differential equations with infinite-dimensional Levy noise. Additionally, we obtain the corresponding periodic measures and…

Probability · Mathematics 2024-12-24 Xinying Deng , Yong Li , Xue Yang

We obtain pointwise ergodic theorems with rate under conditions expressed in terms of the convergence of series involving $\|\sum_{k=1} ^nf\circ \theta^k\|_2$, improving previous results. Then, using known results on martingale…

Probability · Mathematics 2009-04-02 Christophe Cuny

This paper presents some limit theorems for certain functionals of moving averages of semimartingales plus noise which are observed at high frequency. Our method generalizes the pre-averaging approach (see [Bernoulli 15 (2009) 634--658,…

Statistics Theory · Mathematics 2010-10-05 Jean Jacod , Mark Podolskij , Mathias Vetter

We formulate and prove a new sufficient conditions for Central Limit Theorem(CLT) in the space of continuous functions in the terms typical for the approximation theory. We prove that the conditions for continuous CLT obtained by N.C.Jain…

Probability · Mathematics 2013-04-02 E. Ostrovsky , L. Sirota