Journal of the American Statistical Association

最新文章

• Estimating Latent Processes on a Network From Indirect Measurements AI翻译
  作者：Airoldi, Edoardo M.; Blocker, Alexander W.
  作者单位：Harvard University; Harvard University
  摘要：In a communication network, point-to-point traffic volumes over time are critical for designing protocols that route information efficiently and for maintaining security, whether at the scale of an Internet service provider or within a corporation. While technically feasible, the direct measurement of point-to-point traffic imposes a heavy burden on network performance and is typically not implemented. Instead, indirect aggregate traffic volumes are routinely collected. We consider the problem...
• Misspecification Testing in a Class of Conditional Distributional Models AI翻译
  作者：Rothe, Christoph; Wied, Dominik
  作者单位：Columbia University; Dortmund University of Technology
  摘要：We propose a specification test for a wide range of parametric models for the conditional distribution function of an outcome variable given a vector of covariates. The test is based on the Cramer-von Mises distance between an unrestricted estimate of the joint distribution function of the data and a restricted estimate that imposes the structure implied by the model. The procedure is straightforward to implement, is consistent against fixed alternatives, has nontrivial power against local dev...
• Validation of Visual Statistical Inference, Applied to Linear Models AI翻译
  作者：Majumder, Mahbubul; Hofmann, Heike; Cook, Dianne
  作者单位：Iowa State University
  摘要：Statistical graphics play a crucial role in exploratory data analysis, model checking, and diagnosis. The lineup protocol enables statistical significance testing of visual findings, bridging the gulf between exploratory and inferential statistics. In this article, inferential methods for statistical graphics are developed further by refining the terminology of visual inference and framing the lineup protocol in a context that allows direct comparison with conventional tests in scenarios when ...
• Tracking Multiple Targets Using Binary Decisions From Wireless Sensor Networks AI翻译
  作者：Katenka, Natallia; Levina, Elizaveta; Michailidis, George
  作者单位：University of Rhode Island; University of Michigan System; University of Michigan
  摘要：This article introduces a framework for tracking multiple targets over time using binary decisions collected by a wireless sensor network, and applies the methodology to two case studies-an experiment involving tracking people and a dataset adapted from a project tracking zebras in Kenya. The tracking approach is based on a penalized maximum likelihood framework, and allows for sensor failures, targets appearing and disappearing over time, and complex intersecting target trajectories. We show ...
• Oracally Efficient Two-Step Estimation of Generalized Additive Model AI翻译
  作者：Liu, Rong; Yang, Lijian; Haerdle, Wolfgang K.
  作者单位：Soochow University - China; University System of Ohio; University of Toledo; Michigan State University; Humboldt University of Berlin
  摘要：The generalized additive model (GAM) is a multivariate nonparametric regression tool for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions and the constant, which are oracally efficient under weak dependence. The SBK technique is both computationally expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic ...
• Robust Variable Selection With Exponential Squared Loss AI翻译
  作者：Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping
  作者单位：Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Shanghai University of Finance & Economics; Yale University
  摘要：Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this article, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is th...
• A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications AI翻译
  作者：Mitra, Riten; Mueller, Peter; Liang, Shoudan; Yue, Lu; Ji, Yuan
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; NorthShore University Health System
  摘要：Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-exist and co-regulate biological processes such as gene expression and, therefore, are intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model using ChIP-Seq data. A critical computational hurdle in the inference for the proposed graphical model is the evaluation of a normalization constant in an autologisti...
• Two-Stage Importance Sampling With Mixture Proposals AI翻译
  作者：Li, Wentao; Tan, Zhiqiang; Chen, Rong
  作者单位：Lancaster University; Rutgers University System; Rutgers University New Brunswick
  摘要：For importance sampling (IS), multiple proposals can be combined to address different aspects of a target distribution. There are various methods for IS with multiple proposals, including Hesterberg's stratified IS estimator, Owen and Zhou's regression estimator, and Tan's maximum likelihood estimator. For the problem of efficiently allocating samples to different proposals, it is natural to use a pilot sample to select the mixture proportions before the actual sampling and estimation. However...
• Asymptotic Equivalence of Regularization Methods in Thresholded Parameter Space AI翻译
  作者：Fan, Yingying; Lv, Jinchi
  作者单位：University of Southern California
  摘要：High-dimensional data analysis has motivated a spectrum of regularization methods for variable selection and sparse modeling, with two popular methods being convex and concave ones. A long debate has taken place on whether one class dominates the other, an important question both in theory and to practitioners. In this article, we characterize the asymptotic equivalence of regularization methods, with general penalty functions, in a thresholded parameter space under the generalized linear mode...
• Sensitivity Analysis of Per-Protocol Time-to-Event Treatment Efficacy in Randomized Clinical Trials AI翻译
  作者：Gilbert, Peter B.; Shepherd, Bryan E.; Hudgens, Michael G.
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; Vanderbilt University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Assessing per-protocol (PP) treatment efficacy on a time-to-event endpoint is a common objective of randomized clinical trials. The typical analysis uses the same method employed for the intention-to-treat analysis (e.g., standard survival analysis) applied to the subgroup meeting protocol adherence criteria. However, due to potential post-randomization selection bias, this analysis may mislead about treatment efficacy. Moreover, while there is extensive literature on methods for assessing cau...