Journal of the American Statistical Association

最新文章

• Sparse Convoluted Rank Regression in High Dimensions AI翻译
  作者：Zhou, Le; Wang, Boxiang; Zou, Hui
  作者单位：Hong Kong Baptist University; University of Iowa; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Wang et al. studied the high-dimensional sparse penalized rank regression and established its nice theoretical properties. Compared with the least squares, rank regression can have a substantial gain in estimation efficiency while maintaining a minimal relative efficiency of 86.4%. However, the computation of penalized rank regression can be very challenging for high-dimensional data, due to the highly nonsmooth rank regression loss. In this work we view the rank regression loss as a nonsmooth...
• Bayesian Robustness: A Nonasymptotic Viewpoint AI翻译
  作者：Bhatia, Kush; Ma, Yi-An; Dragan, Anca D.; Bartlett, Peter L.; Jordan, Michael I.
  作者单位：Stanford University; University of California System; University of California San Diego; University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：We study the problem of robustly estimating the posterior distribution for the setting where observed data can be contaminated with potentially adversarial outliers. We propose Rob-ULA, a robust variant of the Unadjusted Langevin Algorithm (ULA), and provide a finite-sample analysis of its sampling distribution. In particular, we show that after T = O (d/eacc) iterations, we can sample from pT such that dist(pT, p*) = e(acc) + O(e), where e is the fraction of corruptions and dist represents th...
• Local Signal Detection on Irregular Domains with Generalized Varying Coefficient Models AI翻译
  作者：Zhang, Chengzhu; Xue, Lan; Chen, Yu; Lian, Heng; Qu, Annie
  作者单位：Oregon State University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; City University of Hong Kong; University of California System; University of California Irvine
  摘要：In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This article focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate nonparametric varyi...
• A Composite Likelihood-Based Approach for Change-Point Detection in Spatio-Temporal Processes AI翻译
  作者：Zhao, Zifeng; Ma, Ting Fung; Ng, Wai Leong; Yau, Chun Yip
  作者单位：University of Notre Dame; University of South Carolina System; University of South Carolina Columbia; Hang Seng University of Hong Kong; Chinese University of Hong Kong
  摘要：This article develops a unified and computationally efficient method for change-point estimation along the time dimension in a nonstationary spatio-temporal process. By modeling a nonstationary spatio-temporal process as a piecewise stationary spatio-temporal process, we consider simultaneous estimation of the number and locations of change-points, and model parameters in each segment. A composite likelihood-based criterion is developed for change-point and parameter estimation. Under the fram...
• Graphical Principal Component Analysis of Multivariate Functional Time Series AI翻译
  作者：Tan, Jianbin; Liang, Decai; Guan, Yongtao; Huang, Hui
  作者单位：Sun Yat Sen University; Nankai University; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Renmin University of China; Renmin University of China
  摘要：In this article, we consider multivariate functional time series with a two-way dependence structure: a serial dependence across time points and a graphical interaction among the multiple functions within each time point. We develop the notion of dynamic weak separability, a more general condition than those assumed in literature, and use it to characterize the two-way structure in multivariate functional time series. Based on the proposed weak separability, we develop a unified framework for ...
• Large Scale Prediction with Decision Trees AI翻译
  作者：Klusowski, Jason M.; Tian, Peter M.
  作者单位：Princeton University
  摘要：This article shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number of predictor variables grows sub-exponentially with the sample size, under natural 0-norm and 1-norm sparsity constraints. The theory applies to a wide range of models, including (ordinary or logistic) additive regression models with component functions that are continuous, of bounded variation, or, ...
• Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution AI翻译
  作者：Mork, Daniel; Kioumourtzoglou, Marianthi-Anna; Weisskopf, Marc; Coull, Brent A.; Wilson, Ander
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Columbia University; Harvard University; Harvard T.H. Chan School of Public Health; Colorado State University System; Colorado State University Fort Collins
  摘要：Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility-periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birt...
• A Nonstationary Soft Partitioned Gaussian Process Model via Random Spanning Trees AI翻译
  作者：Luo, Zhao Tang; Sang, Huiyan; Mallick, Bani
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：There has been a long-standing challenge in developing locally stationary Gaussian process models concerning how to obtain flexible partitions and make predictions near boundaries. In this work, we develop a new class of locally stationary stochastic processes, where local partitions are modeled by a soft partition process via predictive random spanning trees that leads to highly flexible spatially contiguous subregion shapes. This valid nonstationary process model knits together local models ...
• Inference in High-Dimensional Online Changepoint Detection AI翻译
  作者：Chen, Yudong; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; University of London; London School Economics & Political Science
  摘要：We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices of coordinates in which the mean changes. We propose an online algorithm that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor. Th...
• Network Inference Using the Hub Model and Variants AI翻译
  作者：He, Zhibing; Zhao, Yunpeng; Bickel, Peter; Weko, Charles; Cheng, Dan; Wang, Jirui
  作者单位：Arizona State University; Arizona State University-Tempe; University of California System; University of California Berkeley; Arizona State University; Arizona State University-Tempe
  摘要：Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a me...