We define two minimum distance estimators for dependent data by minimizing some approximated Maximum Mean Discrepancy distances between the true empirical distribution of observations and their assumed (parametric) model distribution. When the latter one is intractable, it is approximated by simulation, allowing to accommodate most dynamic processes with latent variables. We derive the non-asymptotic and the large sample properties of our estimators in the context of absolutely regular/beta-mixing random elements. Our simulation experiments illustrate the robustness of our procedures to model misspecification, particularly in comparison with alternative standard estimation methods.

翻译：我们通过最小化观测值的真实经验分布与其假设（参数）模型分布之间的近似最大均值差异距离，为相依数据定义了两类最小距离估计量。当后者难以处理时，通过模拟进行近似，从而能够适应大多数具有隐变量的动态过程。我们在绝对正则/β混合随机元素的背景下，推导了估计量的非渐近性质与大样本性质。模拟实验表明，相较于其他标准估计方法，我们的方法对模型设定错误具有鲁棒性。