Modeling and Optimal Inference in change-point models with ultra-high dimensional data

关于多变量或张量设置中变点的建模和推断出现在许多统计应用领域，如金融市场、神经成像、计量经济学、社会网络分析和反恐。这项建议涉及发展这样的方法在随机微分方程(SDE)，分数随机微分方程(FSDE)，常微分方程组(ODE)。它是 由三个主要部分组成：变化的SDE和fSDE模型的稳健推理，带变点的高维张量回归的改进推理，以及动态系统的常微分方程组建模和推理。 在第一部分中，我将研究一些具有未知倍数的多元随机微分方程的推断问题 漂移参数可以满足一定限制的变点。 我将在fSDE的背景下研究类似的推理问题。在这两种情况下，我都会考虑漂移系数不是 必然是线性的。因此，这些方法将适用于下列特殊情况 数据集由广义CoxIngersollRoss过程或Ornstein-Uhlenbeck过程生成。上述问题也将在政权转换的背景下进行研究。除了处理由于变点的存在和参数先验知识的不确定性而引入这些模型的复杂性外，我还计划为估计器本身的维度是随机的情况开发渐近结果。 在第二部分中，我将考虑张量的推理问题 张量参数为时的多个变点回归模型 怀疑是为了满足某些限制。我也会研究类似的问题 在高维数据的上下文中。与最近几年的类似模型相比 文学，我会放宽对误差项的条件，所以 它不需要是独立的和相同分布的。这种依赖 结构将至多是混合型的。我也会考虑这件事 其中误差项是长记忆静止过程。 在第三部分中，我将考虑动力系统的随机版本的建模，例如那些出现在生态或生物医学系统中的系统，这些系统通常由常微分方程组建模。我将首先得到常微分方程组轨迹的初步非参数估计，然后用它们来构造估计函数来对常微分方程组的参数进行推断。这些推理工具随后将用于检测此类系统中的多个变化点。同样在这种情况下，我将把通常使用的关于误差独立性的强假设放宽到较弱的混合相关性。例如，在生态系统中，所提出的模型有望考虑一些现实因素，如动物的适应、迁徙和/或隐藏策略。

Modeling and inference about change-points in multivariate or tensor setups appear in many areas of statistical applications such as financial markets, neuroimaging, econometrics, social network analysis and counter-terrorism. This proposal is concerned with developing such methodologies in stochastic differential equations (SDE), fractional stochastic differential equations (fSDE), ordinary differential equations (ODEs). It is composed of three main parts: robust inference in SDE and fSDE models subject to changes, improved inference in high dimensional tensor regression with change-points, and modeling and inference in dynamical systems via ODEs. In Part 1, I will study inference problems in some multivariate SDE with unknown multiple change-points for which the drift parameters may satisfy some restrictions. I will study similar inference problems in context of fSDE. In both cases, I will consider the general case where the drift coefficient is not necessarily linear. Thus, the methods will be applicable to special cases where the datasets are generated by the generalized CoxIngersollRoss processes or Ornstein-Uhlenbeck processes. The above problems will also be studied in the context of regime switching. In addition to dealing with complexities brought into these models by the presence of change-points and by the uncertainty in the prior knowledge about the parameters, I plan to develop asymptotic results for the situation where the dimensions of the estimators themselves are random. In Part 2, I will consider inference problems in tensor regression models with multiple change-points when the tensor parameter is suspected to satisfy some restriction. I will also study similar problems in the context of high dimensional data. As compared to similar models in recent literature, I will relax the conditions on the error term so that it does not need to be independent and identically distributed. The dependence structure will be at most that of mixingale. I will also consider the case where the error term is a long memory stationary process. In Part 3, I will consider the modeling of stochastic versions of dynamical systems such as those appearing in ecological or biomedical systems which are commonly modeled by ODEs. I will first obtain preliminary nonparametric estimators of the trajectory of the ODEs and then use them to construct estimating functions to make inferences about the parameters of the ODEs. These inferential tools will then be used to detect multiple change-points in such systems. Also in this case, I will relax the commonly used strong assumptions of independence of the errors to the weaker mixingale dependence. For instance, in ecological systems, the proposed models are expected to account for some realistic factors such as animal adaptation, migration and/or hiding strategies.