Journal of the American Statistical Association

最新文章

• Optimal Estimation of the Number of Network Communities AI翻译
  作者：Jin, Jiashun; Ke, Zheng Tracy; Luo, Shengming; Wang, Minzhe
  作者单位：Carnegie Mellon University; Harvard University
  摘要：In network analysis, how to estimate the number of communities K is a fundamental problem. We consider a broad setting where we allow severe degree heterogeneity and a wide range of sparsity levels, and propose Stepwise Goodness of Fit (StGoF) as a new approach. This is a stepwise algorithm, where for m = 1, 2, ..., we alternately use a community detection step and a goodness of fit (GoF) step. We adapt SCORE Jin for community detection, and propose a new GoF metric. We show that at step m, th...
• Score Matching for Compositional Distributions AI翻译
  作者：Scealy, Janice L.; Wood, Andrew T. A.
  作者单位：Australian National University
  摘要：Compositional data are challenging to analyse due to the non-negativity and sum-to-one constraints on the sample space. With real data, it is often the case that many of the compositional components are highly right-skewed, with large numbers of zeros. Major limitations of currently available models for compositional data include one or more of the following: insufficient flexibility in terms of distributional shape; difficulty in accommodating zeros in the data in estimation; and lack of comp...
• Causal Spillover Effects Using Instrumental Variables AI翻译
  作者：Vazquez-Bare, Gonzalo
  作者单位：University of California System; University of California Santa Barbara
  摘要：I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally in...
• Classification Trees for Imbalanced Data: Surface-to-Volume Regularization AI翻译
  作者：Zhu, Yichen; Li, Cheng; Dunson, David B.
  作者单位：Duke University; National University of Singapore; Duke University
  摘要：Classification algorithms face difficulties when one or more classes have limited training data. We are particularly interested in classification trees, due to their interpretability and flexibility. When data are limited in one or more of the classes, the estimated decision boundaries are often irregularly shaped due to the limited sample size, leading to poor generalization error. We propose a novel approach that penalizes the Surface-to-Volume Ratio (SVR) of the decision set, obtaining a ne...
• Bayesian Conjugacy in Probit, Tobit, Multinomial Probit and Extensions: A Review and New Results AI翻译
  作者：Anceschi, Niccolo; Fasano, Augusto; Durante, Daniele; Zanella, Giacomo
  作者单位：Bocconi University; Bocconi University
  摘要：A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including classical Gaussian response settings, this class also encompasses probit, multinomial probit and tobit regression, among others, thereby yielding one of the most widely-implemented families of models in routine applications. The relevance of such representations has stimulated decades of research in the Bayesian field, mostly motivate...
• Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models AI翻译
  作者：Mohammadi, Reza; Massam, Helene; Letac, Gerard
  作者单位：University of Amsterdam; York University - Canada; Universite de Toulouse; Universite Toulouse III - Paul Sabatier
  摘要：Bayesian structure learning in Gaussian graphical models is often done by search algorithms over the graph space.The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wishart.With this prior, the transition probabilities in the search algorithms necessitate evaluating the ratios of the prior normalizing constants of G-Wishart.In moderate to high-dimensions, this ratio is often approximated by using sampling-based methods as computationally expensive ...
• A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models AI翻译
  作者：Tuo, Rui; He, Shiyuan; Pourhabib, Arash; Ding, Yu; Huang, Jianhua Z.
  作者单位：Texas A&M University System; Texas A&M University College Station; Renmin University of China; Oklahoma State University System; Oklahoma State University - Stillwater; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
  摘要：This article develops a frequentist solution to the functional calibration problem, where the value of a calibration parameter in a computer model is allowed to vary with the value of control variables in the physical system. The need of functional calibration is motivated by engineering applications where using a constant calibration parameter results in a significant mismatch between outputs from the computer model and the physical experiment. Reproducing kernel Hilbert spaces (RKHS) are use...
• A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data AI翻译
  作者：Denti, Francesco; Camerlenghi, Federico; Guindani, Michele; Mira, Antonietta
  作者单位：University of California System; University of California Irvine; University of Milano-Bicocca; Universita della Svizzera Italiana; University of Insubria; University of Milano-Bicocca; Collegio Carlo Alberto; Bocconi University
  摘要：The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is...
• Capture-Recapture Models with Heterogeneous Temporary Emigration AI翻译
  作者：Matechou, Eleni; Argiento, Raffaele
  作者单位：University of Kent; University of Bergamo
  摘要：We propose a novel approach for modeling capture-recapture (CR) data on open populations that exhibit temporary emigration, while also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities between individuals. Our modeling approach combines changepoint processes-fitted using an adaptive approach-for inferring individual visits, with Bayesian mixture modeling-fitted using a nonparametric approach-for identifying dusters of individuals with ...
• Understanding Implicit Regularization in Over-Parameterized Single Index Model AI翻译
  作者：Fan, Jianqing; Yang, Zhuoran; Yu, Mengxin
  作者单位：Princeton University; Yale University
  摘要：In this article, we leverage over-parameterization to design regularization-free algorithms for the high-dimensional single index model and provide theoretical guarantees for the induced implicit regularization phenomenon. Specifically, we study both vector and matrix single index models where the link function is nonlinear and unknown, the signal parameter is either a sparse vector or a low-rank symmetric matrix, and the response variable can be heavy-tailed. To gain a better understanding of...