4期

最新文章

• Proposer of the vote of thanks to Rohe & Zeng and contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' AI翻译
  作者：Hoff, Peter
  作者单位：Duke University; Duke University
• Testing for the Markov property in time series via deep conditional generative learning AI翻译
  作者：Zhou, Yunzhe; Shi, Chengchun; Li, Lexin; Yao, Qiwei
  作者单位：University of California System; University of California Berkeley; University of London; London School Economics & Political Science; University of London; London School Economics & Political Science
  摘要：The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of paramount importance. In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning. We also apply the test sequentially to determine the order of the Markov model. We show that the test controls the type-I error asymptotically, and has...
• Kuldeep Kumar's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Kumar, Kuldeep
  作者单位：Bond University
• Vintage factor analysis with Varimax performs statistical inference AI翻译
  作者：Pilling, Mark
  作者单位：University of Cambridge
• Karl Rohe and Muzhe Zeng's reply to the Discussion of 'Vintage factor analysis with varimax performs statistical inference' AI翻译
  作者：Rohe, Karl; Zeng, Muzhe
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• Quantile autoregressive conditional heteroscedasticity AI翻译
  作者：Zhu, Qianqian; Tan, Songhua; Zheng, Yao; Li, Guodong
  作者单位：Shanghai University of Finance & Economics; University of Connecticut; University of Hong Kong
  摘要：This article proposes a novel conditional heteroscedastic time series model by applying the framework of quantile regression processes to the ARCH(& INFIN;) form of the GARCH model. This model can provide varying structures for conditional quantiles of the time series across different quantile levels, while including the commonly used GARCH model as a special case. The strict stationarity of the model is discussed. For robustness against heavy-tailed distributions, a self-weighted quantile reg...
• Christine P Chai's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Chai, Christine P.
  作者单位：Microsoft
• Vintage factor analysis with Varimax performs statistical inference AI翻译
  作者：Zhou, Ying; Zhang, Xinyi
  作者单位：University of Toronto
• Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：He, Yinqiu; Gu, Yuqi; Ying, Zhiliang
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Columbia University; Columbia University
• Root and community inference on the latent growth process of a network AI翻译
  作者：Crane, Harry; Xu, Min
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erd & odblac;s-R & eacute;nyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erd & odblac;s-R & eacute;nyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER comp...