541期

最新文章

• The Generalized Oaxaca-Blinder Estimator AI翻译
  作者：Guo, Kevin; Basse, Guillaume
  作者单位：Stanford University
  摘要：After performing a randomized experiment, researchers often use ordinary least-square (OLS) regression to adjust for baseline covariates when estimating the average treatment effect. It is widely known that the resulting confidence interval is valid even if the linear model is misspecified. In this article, we generalize that conclusion to covariate adjustment with nonlinear models. We introduce an intuitive way to use any simple nonlinear model to construct a covariate-adjusted confidence int...
• Structure-Adaptive Sequential Testing for Online False Discovery Rate Control AI翻译
  作者：Gang, Bowen; Sun, Wenguang; Wang, Weinan
  作者单位：Fudan University; University of Southern California
  摘要：Consider the online testing of a stream of hypotheses where a real-time decision must be made before the next data point arrives. The error rate is required to be controlled at all decision points. Conventional simultaneous testing rules are no longer applicable due to the more stringent error constraints and absence of future data. Moreover, the online decision-making process may come to a halt when the total error budget, or alpha-wealth, is exhausted. This work develops a new class of struc...
• RaSE: A Variable Screening Framework via Random Subspace Ensembles AI翻译
  作者：Tian, Ye; Feng, Yang
  作者单位：Columbia University; New York University
  摘要：Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the predictors according to their individual contributions to the response. As a result, variables that are marginally independent but jointly dependent with the response could be missed. In this work, we propose a new framework for variable screening, random subspace ensemble (RaSE), which works by evaluating the quali...
• 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...
• 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...
• Functional Time Series Prediction Under Partial Observation of the Future Curve AI翻译
  作者：Jiao, Shuhao; Aue, Alexander; Ombao, Hernando
  作者单位：King Abdullah University of Science & Technology; University of California System; University of California Davis
  摘要：This article tackles one of the most fundamental goals in functional time series analysis which is to provide reliable predictions for future functions. Existing methods for predicting a complete future functional observation use only completely observed trajectories. We develop a new method, called partial functional prediction (PFP), which uses both completely observed trajectories and partial information (available partial data) on the trajectory to be predicted. The PFP method includes an ...
• Conditional Functional Graphical Models AI翻译
  作者：Lee, Kuang-Yao; Ji, Dingjue; Li, Lexin; Constable, Todd; Zhao, Hongyu
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Yale University; University of California System; University of California Berkeley; Yale University
  摘要：Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling. Most existing methods focus on estimating the graph by aggregating samples, but largely ignore the subject-level heterogeneity due to the external variables. In this article, we introd...