550期

最新文章

• Large-Scale Low-Rank Gaussian Process Prediction with Support Points AI翻译
  作者：Song, Yan; Dai, Wenlin; Genton, Marc G.
  作者单位：Renmin University of China; King Abdullah University of Science & Technology
  摘要：Low-rank approximation is a popular strategy to tackle the big n problem associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however, theoretical foundations explain...
• Efficient Multiple Change Point Detection and Localization For High-Dimensional Quantile Regression with Heteroscedasticity AI翻译
  作者：Wang, Xianru; Liu, Bin; Zhang, Xinsheng; Liu, Yufeng
  作者单位：Southwestern University of Finance & Economics - China; Fudan University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this article, we consider potential structural changes and complicated tail distributions. There are various existing methods proposed to handle either structural changes or heteroscedasticity. However, it is difficult to handle them simultaneously. To overcome this limitation, we consider statistically and computationally efficient change point detect...
• Deep Regression Learning with Optimal Loss Function AI翻译
  作者：Wang, Xuancheng; Zhou, Ling; Lin, Huazhen
  作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China
  摘要：In this article, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal properties, such as efficie...
• Optimal Network Membership Estimation under Severe Degree Heterogeneity AI翻译
  作者：Ke, Zheng Tracy; Wang, Jingming
  作者单位：Harvard University; University of Virginia
  摘要：Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This article examines the impact of degree heterogeneity on statistical limits of network data analysis. Introducing the heterogeneity distribution (HD) under a degree-corrected mixed membership model, we show that the optimal rate of mixed membership estimation is an explicit functional of the HD. This result confirms that severe degree heterogeneity decelerates the e...
• Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis AI翻译
  作者：Chandra, Noirrit Kiran; Dunson, David B.; Xu, Jason
  作者单位：University of Texas System; University of Texas Dallas; Duke University
  摘要：Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions, for instance in reproducing studies across research groups. In such cases, it is natural to seek to learn the shared versus condition-specific structure. Existing hierarchical extensions of factor analysis have been proposed, but face practical issues includi...
• Robust Estimation for Number of Factors in High Dimensional Factor Modeling via Spearman Correlation Matrix AI翻译
  作者：Qiu, Jiaxin; Li, Zeng; Yao, Jianfeng
  作者单位：University of Hong Kong; Southern University of Science & Technology; The Chinese University of Hong Kong, Shenzhen
  摘要：Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this article, we introduce a new estimator based on the spectral properties of Spearman sample correlation matrix under the high-dimensional setting, where both dimension and sample size tend to infinity proportionally. Our estimator is robust against heavy tails in either the common factors or idiosyncratic errors. The consistency of our estimator i...
• Synthetic Likelihood in Misspecified Models AI翻译
  作者：Frazier, David T.; Nott, David J.; Drovandi, Christopher
  作者单位：Monash University; National University of Singapore; National University of Singapore; Queensland University of Technology (QUT)
  摘要：Bayesian synthetic likelihood is a widely used approach for conducting Bayesian analysis in complex models where evaluation of the likelihood is infeasible but simulation from the assumed model is tractable. We analyze the behavior of the Bayesian synthetic likelihood posterior when the assumed model differs from the actual data generating process. We demonstrate that the Bayesian synthetic likelihood posterior can display a wide range of nonstandard behaviors depending on the level of model m...
• Population-Level Balance in Signed Networks AI翻译
  作者：Tang, Weijing; Zhu, Ji
  作者单位：Carnegie Mellon University; University of Michigan System; University of Michigan
  摘要：Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely unexplored. In signed networks, there exist both positive (e.g., like, trust) and negative (e.g., dislike, distrust) edges, which are commonly seen in real-world scenarios. The positive and negative edges in signed networks lead to unique structural patterns, which pose challenges for statistical mo...
• Distributed Heterogeneity Learning for Generalized Partially Linear Models with Spatially Varying Coefficients AI翻译
  作者：Yu, Shan; Wang, Guannan; Wang, Li
  作者单位：University of Virginia; William & Mary; George Mason University
  摘要：Spatial heterogeneity is of great importance in social, economic, and environmental science studies. The spatially varying coefficient model is a popular and effective spatial regression technique to address spatial heterogeneity. However, accounting for heterogeneity comes at the cost of reducing model parsimony. To balance flexibility and parsimony, this article develops a class of generalized partially linear spatially varying coefficient models which allow the inclusion of both constant an...
• Model-Based Causal Feature Selection for General Response Types AI翻译
  作者：Kook, Lucas; Saengkyongam, Sorawit; Lundborg, Anton Rask; Hothorn, Torsten; Peters, Jonas
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen; Swiss School of Public Health (SSPH+); University of Zurich
  摘要：Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters, B & uuml;hlmann, and Meinshausen) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor Type I err...