Journal of the American Statistical Association

最新文章

• Fisher-Pitman Permutation Tests Based on Nonparametric Poisson Mixtures with Application to Single Cell Genomics AI翻译
  作者：Miao, Zhen; Kong, Weihao; Vinayak, Ramya Korlakai; Sun, Wei; Han, Fang
  作者单位：University of Washington; University of Washington Seattle; Alphabet Inc.; Google Incorporated; University of Wisconsin System; University of Wisconsin Madison; Fred Hutchinson Cancer Center
  摘要：This article investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to adapt to complicated unspecified structures of count data and also consistent against their corresponding ANOVA-type alternatives; the latter is a result in parallel to classic c...
• Policy Optimization Using Semiparametric Models for Dynamic Pricing AI翻译
  作者：Fan, Jianqing; Guo, Yongyi; Yu, Mengxin
  作者单位：Princeton University
  摘要：In this article, we study the contextual dynamic pricing problem where the market value of a product is linear in its observed features plus some market noise. Products are sold one at a time, and only a binary response indicating success or failure of a sale is observed. Our model setting is similar to the work by Javanmard and Nazerzadeh except that we expand the demand curve to a semiparametric model and learn dynamically both parametric and nonparametric components. We propose a dynamic st...
• An Additive Graphical Model for Discrete Data AI翻译
  作者：Tao, Jun; Li, Bing; Xue, Lingzhou
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three-way statistical relation that shares similar properties with conditional independence by satisfying the semi-graphoid axioms. Based on this relation we build an additive graphical model for discrete variables that does not suffer from the restriction of a parametric model such as the Ising model. We develop an estimator of the new gra...
• Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection AI翻译
  作者：Chen, Yi; Wang, Yining; Fang, Ethan X.; Wang, Zhaoran; Li, Runze
  作者单位：Hong Kong University of Science & Technology; University of Texas System; University of Texas Dallas; Duke University; Northwestern University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendations, online advertisements, and personalized medicine. However, it is very challenging to balance the exploration and exploitation tradeoff. We modify the LinUCB algorithm in doubly growi...
• Spatial Statistics for Data Science: Theory and Practice with R AI翻译
  作者：Lim, Chae Young
  作者单位：Seoul National University (SNU)
• Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models AI翻译
  作者：Choi, Jungjun; Yuan, Ming
  作者单位：Columbia University
  摘要：This article develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated well even when missing is not at random. Taking advantage of this fact, we divide the missing entries into smaller groups and estimate each group via nuclear norm regularization. In addition, we show...
• Dimension Reduction for Frechet Regression AI翻译
  作者：Zhang, Qi; Xue, Lingzhou; Li, Bing
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Frechet regression model (Petersen and Muller) provides a promising framework for regression analysis with metric space-valued responses. In this article, we introduce a flexible sufficient dimension reduction (SDR) method for Frechet regression to achieve two purposes: to mitigate the curse of dimensionality caused by hi...
• Ideal Bayesian Spatial Adaptation AI翻译
  作者：Rockova, Veronika; Rousseau, Judith
  作者单位：University of Chicago; University of Oxford; Universite PSL; Universite Paris-Dauphine
  摘要：Many real-life applications involve estimation of curves that exhibit complicated shapes including jumps or varying-frequency oscillations. Practical methods have been devised that can adapt to a locally varying complexity of an unknown function (e.g., variable-knot splines, sparse wavelet reconstructions, kernel methods or trees/forests). However, the overwhelming majority of existing asymptotic minimaxity theory is predicated on homogeneous smoothness assumptions. Focusing on locally H & oum...
• Semiparametric Proximal Causal Inference AI翻译
  作者：Cui, Yifan; Pu, Hongming; Shi, Xu; Miao, Wang; Tchetgen, Eric Tchetgen
  作者单位：Zhejiang University; University of Pennsylvania; University of Michigan System; University of Michigan; Peking University
  摘要：Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator's ability to accurately measure covariates that capture all potential sources of confounding. In practice, the most one can hope for is that covariate measurements are at best proxies of the true underlying confounding mechanism operating in a given observational study. In this arti...
• Nonparametric Two-Sample Tests of High Dimensional Mean Vectors via Random Integration AI翻译
  作者：Jiang, Yunlu; Wang, Xueqin; Wen, Canhong; Jiang, Yukang; Zhang, Heping
  作者单位：Jinan University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Sun Yat Sen University; Yale University
  摘要：Testing the equality of the means in two samples is a fundamental statistical inferential problem. Most of the existing methods are based on the sum-of-squares or supremum statistics. They are possibly powerful in some situations, but not in others, and they do not work in a unified way. Using random integration of the difference, we develop a framework that includes and extends many existing methods, especially in high-dimensional settings, without restricting the same covariance matrices or ...