Randomization/virtual-re-sampling methods, Changepoint detection, Short and long memory processes, Self-normalized partial sums processes, Planar random walks, Strong and weak approximations

随机化/虚拟重采样方法、变化点检测、短记忆过程和长记忆过程、自归一化部分和过程、平面随机游走、强近似和弱近似

• 批准号: RGPIN-2016-06167
• 负责人: Csorgo, Miklos
• 金额: $ 1.97万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648369
• 关键词: Randomization; virtual; re; sampling; methods

This PROPOSAL consists of four distinct research plans that are based on recent advances made in each, in the publications of the applicant with his collaborators. PROPOSAL (1) is concerned with exploring the nature of information contained in a sample of observations that are of interest to us, like for example incidence of a disease in epidemiology. We consider the problem of making inference when the available data is either small or big. For the case of having a relatively small sample, we propose a manifold extension of our recently established randomization technique that yields a significantly smaller magnitude of error of inference as compared to that of the classical procedures under the same conditions. Our randomization technique will provide a direct efficient alternative route to a well known computationally intensive randomization technique that is based on repeated re-sampling, that we are to avoid. When the data set is too big to be processed, or when it constitutes a random sample from a super-population, we are to use our randomization technique to base inference on significantly smaller sub-samples. We believe that the anticipated outcomes of this research will be important to theoretical and applied statisticians alike, and will also benefit the theory and practice of data processing for inference in various research fields of interest. PROPOSAL (2) is to explore the possibility of direct applications of our recent achievements in estimating the theoretical mean (average) of observations when it can exist at all, via self (random)- normalization of sums of observations of a random sample. This will be done by making simulations of interest feasible by representing the theoretical outcomes of our recent results in terms of random entities whose properties are, in principle, computable. We'll also explore the possibility of adapting the randomization techniques of PROPOSAL (1) to the context in hand. The anticipated significance of this work is similar to that of PROPOSAL (1). Our PROPOSAL (3) on random walks and Brownian motion on a spider graph, a 'picture' of a spider with a number of legs, will contribute to basic knowledge in mathematical sciences that may eventually become useful in studying complex systems in different areas of science and internet traffic models. The already ongoing studies of PROPOSAL (4) have been inspired by important papers that, in part, were motivated by the so-called transport phenomena of statistical physics, i.e., all irreversible processes of statistical nature stemming from the random continuous motion of molecules, mostly observed in fluids. We hope to have a fairly comprehensive comparative analysis of two approaches to anisotropic random walks that have been dominant in modeling transport phenomena. **

这项提案由四个不同的研究计划组成，每个计划都基于申请人及其合作者在出版物中取得的最新进展。提案(1)涉及探索我们感兴趣的观察样本中所包含的信息的性质，例如流行病学中的疾病发生率。我们考虑了当可用数据无论是小数据还是大数据时进行推理的问题。对于样本相对较小的情况，我们提出了最近建立的随机化技术的流形扩展，与相同条件下的经典过程相比，该技术产生的推断误差要小得多。我们的随机化技术将为众所周知的计算密集型随机化技术提供一种直接有效的替代途径，该随机化技术基于重复重新采样，我们将避免这种技术。当数据集太大而无法处理时，或者当它构成一个来自超级总体的随机样本时，我们将使用我们的随机化技术来基于小得多的子样本进行推断。我们相信，这项研究的预期结果将对理论和应用统计学家具有重要意义，也将有助于在各种感兴趣的研究领域进行推断的数据处理的理论和实践。建议(2)是通过对随机样本的观测和进行自身(随机)归一化，探索直接应用我们最近在估计观测的理论平均值(平均值)方面取得的成果的可能性，如果它确实存在的话。这将通过用随机实体来表示我们最近结果的理论结果来实现感兴趣的模拟，这些随机实体的属性原则上是可计算的。我们还将探索使提案(1)的随机化技术适应手头环境的可能性。这项工作的预期意义与提案(1)相似。我们关于蜘蛛图上的随机行走和布朗运动的建议(3)将有助于数学科学的基本知识，这些知识最终可能在不同科学领域的复杂系统研究和互联网交通模型中有用。蜘蛛图是一幅蜘蛛图，它是一幅有多条腿的蜘蛛图。已经在进行的关于提案(4)的研究受到重要论文的启发，这些论文的部分动机是所谓的统计物理的传输现象，即所有统计性质的不可逆过程源于分子的随机连续运动，主要是在流体中观察到的。我们希望对在模拟交通现象中占主导地位的两种各向异性随机游动的方法进行比较全面的分析。**