U.S.-Hungary Statistics Research: Topics in Change Point and Unit Root Analysis; Rates of Convergence, Permutations and Bootstrap

美国-匈牙利统计研究：变点和单位根分析主题；

• 批准号: 0223262
• 负责人: Lajos Horvath
• 金额: $ 3.25万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0223262&HistoricalAwards=false
• 关键词: U.S.; Hungary; Statistics; Research; Topics

This US-Hungarian collaborative project between Lajos Horvath and Piotr Kokoszka of the University of Utah and their Hungarian partners, Istvan Berkes and Endre Csaki of the A. Renyi Institute of Mathematics in Budapest, examines several statistical and probabilistic problems concerned with the detection and analysis of nonstationarities of various types of stochastic processes. Emphasis is on careful analysis of the rates of convergence of the statistics of interest and on the properties of the associated permutation and bootstrap procedures. Junior researchers from the University of Utah also participate.The joint research plan includes an investigation of change point procedures and the properties of unit root tests, that are to be developed based on resampling and subsampling methods. These are fundamental questions associated with the detection of time series. Results may have practical applications in economics and the financial sector by leading to useful derivations of properties of CUSUM procedures and GARCH models. For example, findings may improve models for stock returns that can be refined to accommodate infinite error variance. This statistics research project fulfills the program objective of advancing scientific knowledge by enabling experts in the United States and Central Europe to combine complementary talents and share research resources in areas of strong mutual interest and competence.

这个由犹他大学的Lajos Horvath和Piotr Kokoszka以及他们的匈牙利合作伙伴，布达佩斯A. Renyi数学研究所的Istvan Berkes和Endre Csaki合作的美国-匈牙利合作项目，研究了与检测和分析各种类型随机过程的非稳定性有关的几个统计和概率问题。重点是仔细分析感兴趣的统计的收敛速度，以及相关排列和自举程序的性质。犹他大学的初级研究人员也参与其中。联合研究计划包括调查变化点程序和单位根检验的性质，这些将基于重抽样和次抽样方法开发。这些都是与时间序列检测相关的基本问题。结果可能在经济学和金融部门有实际应用，导致有用的衍生CUSUM程序和GARCH模型的性质。例如，研究结果可以改进股票收益模型，使其能够适应无限误差方差。该统计研究项目通过使美国和中欧的专家能够在共同感兴趣和能力强的领域结合互补的人才和共享研究资源，实现了推进科学知识的计划目标。