Journal of the American Statistical Association

最新文章

• Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation AI翻译
  作者：Wu, Hau-Tieng; Zhou, Zhou
  作者单位：Duke University; Duke University; University of Toronto
  摘要：We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by nonstationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect al...
• Addressing Multiple Detection Limits with Semiparametric Cumulative Probability Models AI翻译
  作者：Tian, Yuqi; Li, Chun; Tu, Shengxin; James, Nathan T.; Harrell, FrankE.; Shepherd, BryanE.
  作者单位：Vanderbilt University; University of Southern California
  摘要：Detection limits (DLs), where a variable cannot be measured outside of a certain range, are common in research. DLs may vary across study sites or over time. Most approaches to handling DLs in response variables implicitly make strong parametric assumptions on the distribution of data outside DLs. We propose a new approach to deal with multiple DLs based on a widely used ordinal regression model, the cumulative probability model (CPM). The CPM is a rank-based, semiparametric linear transformat...
• Clustering High-Dimensional Noisy Categorical Data AI翻译
  作者：Tian, Zhiyi; Xu, Jiaming; Tang, Jen
  作者单位：IQVIA; Duke University; Purdue University System; Purdue University
  摘要：Clustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorithms can be computationally costly in high dimensions and can struggle with noisy data that has missing values. Furthermore, except for one algorithm, no others provide theoretical guarantees of clustering accuracy. In this article, we propose a general categorical data encoding method and a computatio...
• Asymptotic Distribution-Free Independence Test for High-Dimension Data AI翻译
  作者：Cai, Zhanrui; Lei, Jing; Roeder, Kathryn
  作者单位：University of Hong Kong; Carnegie Mellon University
  摘要：Test of independence is of fundamental importance in modern data analysis, with broad applications in variable selection, graphical models, and causal inference. When the data is high dimensional and the potential dependence signal is sparse, independence testing becomes very challenging without distributional or structural assumptions. In this article, we propose a general framework for independence testing by first fitting a classifier that distinguishes the joint and product distributions, ...
• HAC Covariance Matrix Estimation in Quantile Regression AI翻译
  作者：Galvao, Antonio F.; Yoon, Jungmo
  作者单位：Michigan State University; Hanyang University
  摘要：This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroscedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroscedasticity and autocorrelation consistent (HAC...
• Unveiling the Unobservable: Causal Inference on Multiple Derived Outcomes AI翻译
  作者：Qiu, Yumou; Sun, Jiarui; Zhou, Xiao-Hua
  作者单位：Peking University; Peking University; Peking University; Peking University
  摘要：In many applications, the interest is in treatment effects on random quantities of subjects, where those random quantities are not directly observable but can be estimated based on data from each subject. In this article, we propose a general framework for conducting causal inference in a hierarchical data generation setting. The identifiability of causal parameters of interest is shown under a condition on the biasedness of subject level estimates and an ignorability condition on the treatmen...
• High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling AI翻译
  作者：Cho, Haeran; Maeng, Hyeyoung; Eckley, Idris A.; Fearnhead, Paul
  作者单位：University of Bristol; Durham University; Lancaster University
  摘要：Vector autoregressive (VAR) models are popularly adopted for modeling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modeling, the number of parameters grow quadratically with the dimensionality which necessitates the sparsity assumption in high dimensions. However, it is debatable whether such an assumption is adequate for handling datasets exhibiting strong serial and cross-sectional correlations. We propose a piecewise stationar...
• Efficient Multimodal Sampling via Tempered Distribution Flow AI翻译
  作者：Qiu, Yixuan; Wang, Xiao
  作者单位：Shanghai University of Finance & Economics; Purdue University System; Purdue University
  摘要：Sampling from high-dimensional distributions is a fundamental problem in statistical research and practice. However, great challenges emerge when the target density function is unnormalized and contains isolated modes. We tackle this difficulty by fitting an invertible transformation mapping, called a transport map, between a reference probability measure and the target distribution, so that sampling from the target distribution can be achieved by pushing forward a reference sample through the...
• Federated Offline Reinforcement Learning AI翻译
  作者：Zhou, Doudou; Zhang, Yufeng; Sonabend-W, Aaron; Wang, Zhaoran; Lu, Junwei; Cai, Tianxi
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Northwestern University; Harvard University; Harvard Medical School
  摘要：Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this article, we propose a multi-site Markov decisi...
• Consistent Community Detection in Inter-Layer Dependent Multi-Layer Networks AI翻译
  作者：Zhang, Jingnan; Wang, Junhui; Wang, Xueqin
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese University of Hong Kong
  摘要：Community detection in multi-layer networks, which aims at finding groups of nodes with similar connective patterns among all layers, has attracted tremendous interests in multi-layer network analysis. Most existing methods are extended from those for single-layer networks, which assume that different layers are independent. In this article, we propose a novel community detection method in multi-layer networks with inter-layer dependence, which integrates the stochastic block model (SBM) and t...