548期

最新文章

• A Decorrelating and Debiasing Approach to Simultaneous Inference for High-Dimensional Confounded Models AI翻译
  作者：Sun, Yinrui; Ma, Li; Xia, Yin
  作者单位：Fudan University
  摘要：Motivated by the simultaneous association analysis with the presence of latent confounders, this article studies the large-scale hypothesis testing problem for the high-dimensional confounded linear models with both non-asymptotic and asymptotic false discovery control. Such model covers a wide range of practical settings where both the response and the predictors may be confounded. In the presence of the high-dimensional predictors and the unobservable confounders, the simultaneous inference ...
• Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression AI翻译
  作者：Fan, Jianqing; Gu, Yihong
  作者单位：Princeton University
  摘要：This article introduces a Factor Augmented Sparse Throughput (FAST) model that uses both latent factors and sparse idiosyncratic components for nonparametric regression. It contains many popular statistical models. The FAST model bridges factor models on one end and sparse nonparametric models on the other end. It encompasses structured nonparametric models such as factor augmented additive models and sparse low-dimensional nonparametric interaction models and covers the cases where the covari...
• Identifiability and Consistent Estimation for Gaussian Chain Graph Models AI翻译
  作者：Zhao, Ruixuan; Zhang, Haoran; Wang, Junhui
  作者单位：City University of Hong Kong; Southern University of Science & Technology; Chinese University of Hong Kong
  摘要：The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in the literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this article, we first establish a set of novel identifiability c...
• Causal Inference with Noncompliance and Unknown Interference AI翻译
  作者：Hoshino, Tadao; Yanagi, Takahide
  作者单位：Waseda University; Kyoto University
  摘要：We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate identification conditi...