Journal of the American Statistical Association

最新文章

• Bootstrap Inference in the Presence of Bias AI翻译
  作者：Cavaliere, Giuseppe; Goncalves, Silvia; Nielsen, Morten Orregaard; Zanelli, Edoardo
  作者单位：University of Bologna; McGill University; Aarhus University
  摘要：We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obtained by proper implementations of the bootstrap. Specifically, we show that the prepivoting approach of Beran, originally proposed to deliver higher-order refinements, restores bootstrap validity by transforming the original bootstrap p-value into an asymptotically uniform random variable. We propose two different i...
• Minimum Resource Threshold Policy Under Partial Interference AI翻译
  作者：Park, Chan; Chen, Guanhua; Yu, Menggang; Kang, Hyunseung
  作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocation policy, policymakers may want to distribute them so that at least a certain fraction of individuals in a census block are disease-free and spillover effects due to interference within blocks are accounted for. The article proposes methods to estimate a block-level treatment policy that achieves a predefined, outc...
• Exact Bayesian Inference for Diffusion-Driven Cox Processes AI翻译
  作者：Goncalves, Flavio B.; Latuszynski, Krzysztof G.; Roberts, Gareth O. O.
  作者单位：Universidade Federal de Minas Gerais; University of Warwick
  摘要：In this article, we present a novel methodology to perform Bayesian inference for Cox processes in which the intensity function is driven by a diffusion process. The novelty lies in the fact that no discretization error is involved, despite the non-tractability of both the likelihood function and the transition density of the diffusion. The methodology is based on an MCMC algorithm and its exactness is built on retrospective sampling techniques. The efficiency of the methodology is investigate...
• Gaussian Approximation and Spatially Dependent Wild Bootstrap for High-Dimensional Spatial Data AI翻译
  作者：Kurisu, Daisuke; Kato, Kengo; Shao, Xiaofeng
  作者单位：University of Tokyo; Cornell University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：In this article, we establish a high-dimensional CLT for the sample mean of p-dimensional spatial data observed over irregularly spaced sampling sites in Rd, allowing the dimension p to be much larger than the sample size n. We adopt a stochastic sampling scheme that can generate irregularly spaced sampling sites in a flexible manner and include both pure increasing domain and mixed increasing domain frameworks. To facilitate statistical inference, we develop the spatially dependent wild boots...
• Estimation of Linear Functionals in High-Dimensional Linear Models: From Sparsity to Nonsparsity AI翻译
  作者：Zhao, Junlong; Zhou, Yang; Liu, Yufeng
  作者单位：Beijing Normal University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：High-dimensional linear models are commonly used in practice. In many applications, one is interested in linear transformations beta(T)x of regression coefficients beta epsilon R-p, where x is a specific point and is not required to be identically distributed as the training data. One common approach is the plug-in technique which first estimates beta, then plugs the estimator in the linear transformation for prediction. Despite its popularity, estimation of beta canbe difficult for high-dimen...
• Cross-Validation: What Does It Estimate and How Well Does It Do It? AI翻译
  作者：Bates, Stephen; Hastie, Trevor; Tibshirani, Robert
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Stanford University; Stanford University
  摘要：Cross-validation is a widely used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit to the training data. We prove that this is not the case for the linear model fit by ordinary least squares; rather it estimates the average prediction error of models fit on other unseen training sets drawn from the same population. We further show that th...
• Distributed Inference for Spatial Extremes Modeling in High Dimensions AI翻译
  作者：Hector, Emily C.; Reich, Brian J.
  作者单位：North Carolina State University
  摘要：Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using Max Stable Processes (MSPs) that are computationally prohibitive to fit for as few as a dozen observations. Supposed computationally-efficient approaches like the composite likelihood remain computationally burdensome with a few hundred observations. In this article, we propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers ...
• Kernel Estimation of Bivariate Time-Varying Coefficient Model for Longitudinal Data with Terminal Event AI翻译
  作者：Wang, Yue; Nan, Bin; Kalbfleisch, John D. D.
  作者单位：University of California System; University of California Irvine; University of Michigan System; University of Michigan
  摘要：We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a...
• Inference in Heavy-Tailed Nonstationary Multivariate Time Series AI翻译
  作者：Barigozzi, Matteo; Cavaliere, Giuseppe; Trapani, Lorenzo
  作者单位：University of Bologna; University of Nottingham
  摘要：We study inference on the common stochastic trends in a nonstationary, N-variate time series y(t), in the possible presence of heavy tails. We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trends m based on the eigenvalues of the sample second moment matrix of y(t). We study the rates of such eigenvalues, s...
• Bias-Correction and Test for Mark-Point Dependence with Replicated Marked Point Processes AI翻译
  作者：Xu, Ganggang; Zhang, Jingfei; Li, Yehua; Guan, Yongtao
  作者单位：University of Miami; University of California System; University of California Riverside
  摘要：Mark-point dependence plays a critical role in research problems that can be fitted into the general framework of marked point processes. In this work, we focus on adjusting for mark-point dependence when estimating the mean and covariance functions of the mark process, given independent replicates of the marked point process. We assume that the mark process is a Gaussian process and the point process is a log-Gaussian Cox process, where the mark-point dependence is generated through the depen...