Central limit type theorem and large deviation principle for multi-scale McKean-Vlasov SDEs

被引:11
作者
Hong, Wei [1 ,2 ]
Li, Shihu [1 ]
Liu, Wei [1 ]
Sun, Xiaobin [1 ]
机构
[1] Jiangsu Normal Univ, Sch Math & Stat RIMS, Xuzhou 221116, Peoples R China
[2] Tianjin Univ, Ctr Appl Math, Tianjin 300072, Peoples R China
关键词
Central limit type theorem; Large deviation principle; McKean-Vlasov equation; Poisson equation; Weak convergence approach; REACTION-DIFFUSION EQUATIONS; STOCHASTIC DIFFERENTIAL-EQUATIONS; DISTRIBUTION DEPENDENT SDES; AVERAGING PRINCIPLE; SYSTEMS; CONVERGENCE; DRIVEN; APPROXIMATION; FRAMEWORK; DYNAMICS;
D O I
10.1007/s00440-023-01214-8
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Themain aim of thiswork is to study the asymptotic behavior formulti-scaleMcKeanVlasov stochastic dynamical systems. Firstly, we obtain a central limit type theorem, i.e. the deviation between the slow component X-epsilon and the solution X- of the averaged equation converges weakly to a limiting process. More precisely, X epsilon-X-|root epsilon v e converges weakly in C([0, T], R-n) to the solution of certain distribution dependent stochastic differential equation, which involves an extra explicit stochastic integral term. Secondly, in order to estimate the probability of deviations away from the limiting process, we further investigate the Freidlin-Wentzell's large deviation principle for multi-scale McKean-Vlasov stochastic system when the small-noise regime parameter delta -> 0 and the time scale parameter epsilon(delta) satisfies epsilon(delta)/delta -> 0. The main techniques are based on the Poisson equation for central limit type theorem and the weak convergence approach for large deviation principle.
引用
收藏
页码:133 / 201
页数:69
相关论文
共 77 条
[1]  
[Anonymous], 1956, P 3 BERK S MATH STAT
[2]  
[Anonymous], 2014, Encyclopedia of Mathematics and Its Applications
[3]  
[Anonymous], 2012, Notes on mean field games
[4]   Mean first passage time solution of the Smoluchowski equation: Application to relaxation dynamics in myoglobin [J].
Ansari, A .
JOURNAL OF CHEMICAL PHYSICS, 2000, 112 (05) :2516-2522
[5]  
Arnold L, 2001, PROG PROBAB, V49, P141
[6]   Diffusion approximation for slow motion in fully coupled averaging [J].
Bakhtin, V ;
Kifer, Y .
PROBABILITY THEORY AND RELATED FIELDS, 2004, 129 (02) :157-181
[7]   Bismut formula for Lions derivative of distribution-path dependent SDEs [J].
Bao, Jianhai ;
Ren, Panpan ;
Wang, Feng-Yu .
JOURNAL OF DIFFERENTIAL EQUATIONS, 2021, 282 :285-329
[8]   Large deviations for interacting multiscale particle systems [J].
Bezemek, Z. W. ;
Spiliopoulos, K. .
STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 2023, 155 :27-108
[9]  
Billingsley P., 1999, Convergence of Probability Measures, V2nd
[10]  
Bogoliubov NN., 1961, Asymptotic Methods in the Theory of Nonlinear Oscillations