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报告题目Semiparametric integer-valued autoregressive models on Z

报告人:朱复康 教授

报告摘要In the analysis of real integer-valued time series data, we often encounter negative values and negative correlations. For integer-valued autoregressive time series, there are many parametric models to choose from, but some of them are relatively complex. With little information about the background of real data, we hope that a simple and effective semiparametric model can be used to obtain more information that usually not can be provided by parametric models, such as the confidence interval of the innovation distribution. But the only existing semiparametric model based on thinning operators can only deal with non-negative data with positive correlation coefficients. In addition, it has two drawbacks: first, an initial distribution of the innovation is required, but different initial values may lead to different results; second, the confidence interval of the innovation distribution is not available, which is essential in low-valued data. To overcome these drawbacks, we propose a rounded semiparametric autoregressive model with a log-concave innovation, which can deal with Z-valued time series with autoregressive coefficients of arbitrary sign. The consistencies of the estimators for the parametric and non-parametric parts of the model are also discussed. We illustrate the superior performance of the proposed model based on three real datasets.

报告时间528日 下午 300-400



报告人简介:朱复康,吉林大学数学学院教授、博士生导师,院长助理、系主任。2008年博士毕业,2013年被聘为教授。主要从事时间序列分析和金融统计的研究,已经在Annals of Applied StatisticsJournal of Business & Economic StatisticsStatistica SinicaJournal of Time Series Analysis等期刊上发表论文40余篇。作为负责人获得国家自然科学基金4,现任中国数学会概率统计学会、中国现场统计研究会等学会的常务理事或理事,美国数学会《数学评论》评论员,担任JRSSBJBESAoASBernoulliStatistica SinicaEconometric Theory50余个SCI杂志审稿专家。