[1] Bishwal J P N. Parameter Estimation in Stochastic Differential Equations. Berlin:Springer, 2008 [2] Buckdahn R, Li J, Peng S, Rainer C. Mean-field stochastic differential equations and associated PDEs. Ann Probab, 2017, 45(2):824-878 [3] Butkovsky O. On ergodic properties of nonlinear Markov chains and stochastic McKean-Vlasov equations. Theory Probab Appl, 2014, 58(4):661-674 [4] Eberle A, Guillin A, Zimmer R. Quantitative Harris type theorems for diffusions and McKean-Vlasov processes. arXiv:1606.06012v2, 2017. https://arxiv.org/pdf/1606.06012.pdf [5] Dos Reis G, Salkeld W, Tugaut J. Freidlin-Wentzell LDPs in path space for McKean-Vlasov equations and the functional iterated logarithm law. Ann Appl Probab, 2019, 29(3):1487-1540 [6] Friedman A. Stochastic Differential Equations and Applications. Vol 1. Probability and Mathematical Statistics, Vol. 28. New York-London:Academic Press, INC, 1975 [7] Gloter A, Sørensen M. Estimation for stochastic differential equations with a small diffusion coefficient. Stochastic Process Appl, 2009, 119(3):679-699 [8] Hammersley W, Šiška D, Szpruch L. McKean-Vlasov SDE under measure dependent Lyapunov conditions. arXiv:1802.03974v1, 2018. https://arxiv.org/pdf/1802.03974.pdf [9] Hairer M, Mattingly J C, Scheutzow M. Asymptotic coupling and a general form of Harris theorem with applications to stochastic delay equations. Probab. Theory Related Fields, 2011, 149(1/2):223-259 [10] Hu Y, Long H. Least squares estimator for Ornstein-Uhlenbeck processes driven by α-stable motions. Stochastic Process. Appl, 2009, 119(8):2465-2480 [11] Huang X, Liu C, Wang F-Y. Order preservation for path-distribution dependent SDEs. Commun Pure Appl Anal, 2018, 17(5):2125-2133 [12] Huang X, Röckner M, Wang F-Y. Nonlinear Fokker-Planck equations for probability measures on path space and path-distribution dependent SDEs. arXiv:1709.00556, 2017. https://arxiv.org/pdf/1709.00556.pdf [13] Hutzenthaler M, Jentzen A, Kloeden P E. Strong convergence of an explicit numerical method for SDEs with non-globally Lipschitz continuous coefficients. Ann Appl Probab, 2012, 22(4):1611-1641 [14] Itô K, Nisio M. On stationary solutions of a stochastic differential equation. J Math Kyoto Univ, 1964, 4:1-75 [15] Kutoyants Yu A. Statistical Inference for Ergodic Diffusion Processes. London, Berlin, Heidelberg:SpringerVerlag, 2004 [16] Li J, Min H. Weak solutions of mean-field stochastic differential equations and application to zero-sum stochastic differential games. SIAM J Control Optim, 2016, 54(3):1826-1858 [17] Li J, Wu J-L. On drift parameter estimation for mean-reversion type stochastic differential equations with discrete observations. Adv Difference Equ, 2016, (90):1-23 [18] Liptser R S, Shiryaev A N. Statistics of Random Processes:Ⅱ Applications. Second Edition. Berlin, Heidelberg, New York:Springer-Verlag, 2001 [19] Long H. Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises. Statist Probab Lett, 2009, 79(19):2076-2085 [20] Long H. Parameter estimation for a class of stochastic differential equations driven by small stable noises from discrete observations. Acta Math Sci Ser B Engl Ed, 2010, 30(3):645-663 [21] Long H, Ma C, Shimizu Y. Least squares estimators for stochastic differential equations driven by small Lévy noises. Stochastic Process Appl, 2017, 127(5):1475-1495 [22] Long H, Shimizu Y, Sun W. Least squares estimators for discretely observed stochastic processes driven by small Lévy noises. J Multivariate Anal, 2013, 116:422-439 [23] Ibragimov I A, Hasminskii R Z. Statistical Estimation:Asymptotic Theory. New York, Berlin:SpringerVerlag, 1981 [24] Ma C. A note on "Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises". Statist Probab Lett, 2010, 80(19/20):1528-1531 [25] Masuda H. Simple estimators for non-linear Markovian trend from sampled data:I. ergodic cases. MHF Preprint Series 2005-7, Kyushu University, 2005 [26] McKean H P. A class of Markov processes associated with nonlinear parabolic equations. Proceedings of the National Academy of Sciences, 1966, 56:1907-1911 [27] Mishura Yu S, Veretennikov A Yu. Existence and uniqueness theorems for solutions of McKean-Vlasov stochastic equations. arXiv:1603.02212v4, 2016. https://arxiv.org/pdf/1603.02212.pdf [28] Mohammed S E A. Stochastic Functional Differential Equations. Boston:Pitman, 1984 [29] Prakasa Rao B L S. Statistical Inference for Diffusion Type Processes. Arnold, London, New York:Oxford University Press, 1999 [30] Ren P, Wu J-L. Least squares estimation for path-distribution dependent stochastic differential equations. arXiv:1802.00820, 2018. https://arxiv.org/pdf/1802.00820.pdf [31] Shimizu Y, Yoshida N. Estimation of parameters for diffusion processes with jumps from discrete observations. Stat Inference Stoch Process, 2006, 9(3):227-277 [32] Sørensen M, Uchida M. Small diffusion asymptotics for discretely sampled stochastic differential equations. Bernoulli, 2003, 9(6):1051-1069 [33] Sznitman A S. Topics in propagation of chaos. Ecole d'Ete de Probabilites de Saint-Flour XIX-1989, 165- 251. Lecture Notes in Math, 1464. Berlin:Springer, 1991 [34] Uchida M. Estimation for discretely observed small diffusions based on approximate martingale estimating functions. Scand J Statist, 2004, 31(4):553-566 [35] Uchida M. Approximate martingale estimating functions for stochastic differential equations with small noises. Stochastic Process Appl, 2008, 118(9):1706-1721 [36] van der Vaart A W. Asymptotic Statistics. Cambridge Series in Statistical and Probabilistic Mathematics, Vol. 3. Cambridge:Cambridge University Press, 1998 [37] Veretennikov A Yu. On ergodic measures for McKean-Vlasov stochastic equations//Monte Carlo and QuasiMonte Carlo Methods 2004, 471-486. Berlin:Springer, 2006 [38] Wang F-Y. Estimates for invariant probability measures of degenerate SPDEs with singular and pathdependent drifts. Probab Theory Related Fields, 2018, 172(3/4):1181-1214 [39] Wang F-Y. Distribution dependent SDEs for Landau type equations. Stochastic Process Appl, 2018, 128(2):595-621 |