4期

最新文章

• Filippo Ascolani, Antonio Lijoi and Igor Prünster's contribution to the Discussion of 'Root and community inference on the latent growth process of a network' by Crane and Xu AI翻译
  作者：Ascolani, Filippo; Lijoi, Antonio; Pruenster, Igor
  作者单位：Duke University; Bocconi University
• Testing high-dimensional multinomials with applications to text analysis AI翻译
  作者：Cai, T. Tony; Ke, Zheng T.; Turner, Paxton
  作者单位：University of Pennsylvania; Harvard University
  摘要：Motivated by applications in text mining and discrete distribution inference, we test for equality of probability mass functions of K groups of high-dimensional multinomial distributions. Special cases of this problem include global testing for topic models, two-sample testing in authorship attribution, and closeness testing for discrete distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null hypothesis, is proposed. This parameter-free...
• GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments AI翻译
  作者：Ye, Ting; Liu, Zhonghua; Sun, Baoluo; Tchetgen, Eric Tchetgen
  作者单位：University of Washington; University of Washington Seattle; Columbia University; National University of Singapore; University of Pennsylvania; University of Washington; University of Washington Seattle
  摘要：Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effec...
• Root and community inference on the latent of a network AI翻译
  作者：Banerjee, Sayan
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  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 Erdos-Renyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdos- Renyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Giv...
• Andrej Srakars contribution to the Discussion of Root and community inference on the latent growth process of a network by Crane and Xu AI翻译
  作者：Srakar, Andrej
  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 Erdos-Renyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdos-Renyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Give...
• Seconder of the vote of thanks to Crane and Xu and contribution to the Discussion of 'Root and community inference on the latent growth process of a network' AI翻译
  作者：Rubin-Delanchy, Patrick
  作者单位：University of Edinburgh
• Yicong Jiang and Zheng Tracy Ke's contribution to the Discussion of 'Root and community inference on the latent growth process of a network' by Crane and Xu AI翻译
  作者：Jiang, Yicong; Ke, Zheng Tracy
  作者单位：Harvard University
• Authors' reply to the Discussion of '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
• Qing Yang and Xin Tong's contribution to the Discussion of 'Root and community inference on the latent growth process of a network' by Crane and Xu AI翻译
  作者：Yang, Qing; Tong, Xin
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California
• On the instrumental variable estimation with many weak and invalid instruments AI翻译
  作者：Lin, Yiqi; Windmeijer, Frank; Song, Xinyuan; Fan, Qingliang
  作者单位：Chinese University of Hong Kong; University of Oxford; University of Oxford; Chinese University of Hong Kong; Chinese University of Hong Kong
  摘要：We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the 'sparsest rule', which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs. Furthermore, we propose a surro...