4期

最新文章

• Yunxiao Chen and Gongjun Xu's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Chen, Yunxiao; Xu, Gongjun
  作者单位：University of London; London School Economics & Political Science; University of Michigan System; University of Michigan; University of London; London School Economics & Political Science
• Robust estimation and inference for expected shortfall regression with many regressors AI翻译
  作者：He, Xuming; Tan, Kean Ming; Zhou, Wen-Xin
  作者单位：Washington University (WUSTL); University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：Expected shortfall (ES), also known as superquantile or conditional value-at-risk, is an important measure in risk analysis and stochastic optimisation and has applications beyond these fields. In finance, it refers to the conditional expected return of an asset given that the return is below some quantile of its distribution. In this paper, we consider a joint regression framework recently proposed to model the quantile and ES of a response variable simultaneously, given a set of covariates. ...
• Junhui Cai, Dan Yang, Linda Zhao and Wu Zhu's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Cai, Junhui; Yang, Dan; Zhao, Linda; Zhu, Wu
  作者单位：University of Pennsylvania; University of Hong Kong; Tsinghua University; University of Notre Dame
• Alexander Van Werde's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Van Werde, Alexander
  作者单位：Eindhoven University of Technology; Eindhoven University of Technology
• Vintage Factor Analysis with Varimax Performs Statistical Inference AI翻译
  作者：Wang, Kaizheng
  作者单位：Columbia University; Columbia University
  摘要：In the 1930s, Psychologists began developing Multiple-Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In this form of factor analysis, the Varimax factor rotation redraws the axes through the multi-dimensional factors to make them sparse and thus make them more interpretable. Charles Spearman and many others objected to factor rotations because the factors seem to be rotationally invariant. Despite ...
• Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods AI翻译
  作者：Whitehouse, Michael; Whiteley, Nick; Rimella, Lorenzo
  作者单位：University of Bristol; Lancaster University
  摘要：Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson approximate likelihood (PAL) methods. In contrast to the popular ordinary differential equation (ODE) approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency...
• Rungang Han and Anru R. Zhangs contribution to the Discussion of 'Vintage factor analysis with varimax performs statistical inference' by Rohe & Zeng AI翻译
  作者：Han, Rungang; Zhang, Anru R.
  作者单位：Duke University
• Florian Pargent, David Goretzko and Timo von Oertzen's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Pargent, Florian; Goretzko, David; von Oertzen, Timo
  作者单位：University of Munich; Utrecht University; Max Planck Society; Bundeswehr University Munich; University of Munich
• Seconder 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：Pensky, Marianna
  作者单位：State University System of Florida; University of Central Florida
• Two-stage estimation and bias-corrected empirical likelihood in a partially linear single-index varying-coefficient model AI翻译
  作者：Xue, Liugen
  作者单位：Henan University
  摘要：The estimation and empirical likelihood (EL) of the parameters of interest in a partially linear single-index varying-coefficient model are studied. A two-stage method is presented to estimate the regression parameters and the coefficient functions. The asymptotic distributions of the proposed estimators are obtained. Meanwhile, a bias-corrected EL ratio for the regression parameters is proposed. It is shown that the ratio is asymptotically standard chi-squared. The result can be directly used...