Tobit models for count time series

发布时间：2024-09-12 作者：浏览次数：

Speaker:	朱复康	DateTime:	2024年9月13日(周五) 下午2：45 - 3：45
Brief Introduction to Speaker: 朱复康，吉林大学数学学院教授、博士生导师，吉林国家应用数学中心副主任、院长助理、概率统计与数据科学系主任。2008年博士毕业，2013年破格晋升教授，2021年任唐敖庆领军教授。主要从事时间序列分析和金融统计的研究，已经在Annals of Applied Statistics、Journal of Business & Economic Statistics、Statistica Sinica、Scandinavian Journal of Statistics、Journal of Time Series Analysis、中国科学-数学等期刊上发表论文多篇，主持国家自然科学基金面上项目3项和青年基金1项。曾获得教育部自然科学奖二等奖、吉林省科学技术奖二等奖、长春市有突出贡献专家等奖励，入选美国斯坦福大学发布的全球前2%顶尖科学奖榜单。现任中国现场统计研究会、全国工业统计学教学研究会、中国数学会概率统计分会等学会的理事或常务理事。现任SCI期刊Statistical Papers、Journal of Statistical Computation and Simulation的Associate Editor，是JASA、JRSSB、JBES、AoAS等80余个SCI期刊的匿名审稿人。指导的研究生1人获得吉林省优秀博士学位论文、3人获得吉林省优秀硕士学位论文。		Place:	6号楼二楼报告厅
Abstract:Several models for count time series have been developed during the last decades, often inspired by traditional autoregressive moving average (ARMA) models for real-valued time series, including integer-valued ARMA (INARMA) and integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. Both INARMA and INGARCH models exhibit an ARMA-like autocorrelation function (ACF). To achieve negative ACF values within the class of INGARCH models, log and softplus link functions are suggested in the literature, where the softplus approach leads to conditional linearity in good approximation. However, the softplus approach is limited to the INGARCH family for unbounded counts, i.e., it can neither be used for bounded counts, nor for count processes from the INARMA family. In this paper, we present an alternative solution, named the Tobit approach, for achieving approximate linearity together with negative ACF values, which is more generally applicable than the softp...