Journal of the American Statistical Association

最新文章

• Feature Screening for Interval-Valued Response with Application to Study Association between Posted Salary and Required Skills AI翻译
  作者：Zhong, Wei; Qian, Chen; Liu, Wanjun; Zhu, Liping; Li, Runze
  作者单位：Xiamen University; Virginia Polytechnic Institute & State University; Renmin University of China; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：It is important to quantify the differences in returns to skills using the online job advertisements data, which have attracted great interest in both labor economics and statistics fields. In this article, we study the relationship between the posted salary and the job requirements in online labor markets. There are two challenges to deal with. First, the posted salary is always presented in an interval-valued form, for example, 5k-10k yuan per month. Simply taking the mid-point or the lower ...
• Crop Yield Prediction Using Bayesian Spatially Varying Coefficient Models with Functional Predictors AI翻译
  作者：Park, Yeonjoo; Li, Bo; Li, Yehua
  作者单位：University of Texas System; University of Texas at San Antonio; University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Riverside
  摘要：Reliable prediction for crop yield is crucial for economic planning, food security monitoring, and agricultural risk management. This study aims to develop a crop yield forecasting model at large spatial scales using meteorological variables closely related to crop growth. The influence of climate patterns on agricultural productivity can be spatially inhomogeneous due to local soil and environmental conditions. We propose a Bayesian spatially varying functional model (BSVFM) to predict county...
• Covariate-Assisted Sparse Tensor Completion AI翻译
  作者：Ibriga, Hilda S.; Sun, Will Wei
  作者单位：Purdue University System; Purdue University; Purdue University System; Purdue University
  摘要：We aim to provably complete a sparse and highly missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users' click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on nonmissing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often available. In this ...
• Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization AI翻译
  作者：Cai, Jian-Feng; Li, Jingyang; Xia, Dong
  作者单位：Hong Kong University of Science & Technology
  摘要：We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gra...
• Asymmetric Error Control Under Imperfect Supervision: A Label-Noise-Adjusted Neyman-Pearson Umbrella Algorithm AI翻译
  作者：Yao, Shunan; Rava, Bradley; Tong, Xin; James, Gareth
  作者单位：University of Southern California; University of Southern California
  摘要：Label noise in data has long been an important problem in supervised learning applications as it affects the effectiveness of many widely used classification methods. Recently, important real-world applications, such as medical diagnosis and cybersecurity, have generated renewed interest in the Neyman-Pearson (NP) classification paradigm, which constrains the more severe type of error (e.g., the Type I error) under a preferred level while minimizing the other (e.g., the Type II error). However...
• Test of Weak Separability for Spatially Stationary Functional Field AI翻译
  作者：Liang, Decai; Huang, Hui; Guan, Yongtao; Yao, Fang
  作者单位：Nankai University; Sun Yat Sen University; University of Miami; Peking University
  摘要：For spatially dependent functional data, a generalized Karhunen-Loeve expansion is commonly used to decompose data into an additive form of temporal components and spatially correlated coefficients. This structure provides a convenient model to investigate the space-time interactions, but may not hold for complex spatio-temporal processes. In this work, we introduce the concept of weak separability, and propose a formal test to examine its validity for non-replicated spatially stationary funct...
• False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation AI翻译
  作者：Du, Lilun; Guo, Xu; Sun, Wenguang; Zou, Changliang
  作者单位：Hong Kong University of Science & Technology; Beijing Normal University; University of Southern California; Nankai University
  摘要：We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to contr...
• Stick-Breaking Processes With Exchangeable Length Variables AI翻译
  作者：Gil-Leyva, Maria F.; Mena, Ramses H.
  摘要：Our object of study is the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian nonparametric priors in an unexplored direction. We give conditions to assure the respective species sampling process is proper and the corresponding prior has full support. For a rich subclass we explain how, by tuning a single [0,1]-valued parameter, the stochastic ordering of the weights can be modulated, and Dirichlet and Geometric priors can be reco...
• Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension AI翻译
  作者：Wu, Yunan; Wang, Lan; Fu, Haoda
  作者单位：University of Texas System; University of Texas Dallas; University of Miami; Eli Lilly
  摘要：This article develops new tools to quantify uncertainty in optimal decision making and to gain insight into which variables one should collect information about given the potential cost of measuring a large number of variables. We investigate simultaneous inference to determine if a group of variables is relevant for estimating an optimal decision rule in a high-dimensional semiparametric framework. The unknown link function permits flexible modeling of the interactions between the treatment a...
• Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation AI翻译
  作者：Deb, Nabarun; Sen, Bodhisattva
  作者单位：Columbia University
  摘要：In this article, we propose a general framework for distribution-free nonparametric testing in multi-dimensions, based on a notion of multivariate ranks defined using the theory of measure transportation. Unlike other existing proposals in the literature, these multivariate ranks share a number of useful properties with the usual one-dimensional ranks; most importantly, these ranks are distribution-free. This crucial observation allows us to design nonparametric tests that are exactly distribu...