Journal of the American Statistical Association

最新文章

• Statistical Significance and the Dichotomization of Evidence AI翻译
  作者：McShane, Blakeley B.; Gal, David
  作者单位：Northwestern University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：In light of recent concerns about reproducibility and replicability, the ASA issued a Statement on tanstical Significance and p-values aimed at those who are not primarily statisticians. While the ASA Statement notes that statistical significance and p-values are commonly misused and misinterpreted, it does not discuss and document broader implications of these errors for the interpretation of evidence. In this article, we review research on how applied researchers who are not primarily statis...
• Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods AI翻译
  作者：White, Kyle R.; Stefanski, Leonard A.; Wu, Yichao
  作者单位：North Carolina State University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：This article develops a nonparametric shrinkage and selection estimator via the measurement error selection likelihood approach recently proposed by Stefanski, Wu, and White. The measurement error kernel regression operator (MEKRO) has the same form as the Nadaraya-Watson kernel estimator, but optimizes a measurement error model selection likelihood to estimate the kernel bandwidths. Much like LASSO or COSSO solution paths, MEKRO results in solution paths depending on a tuning parameter that c...
• Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing Comment AI翻译
  作者：Wood, Simon N.
  作者单位：University of Bristol
• Tukey g-and-h Random Fields AI翻译
  作者：Xu, Ganggang; Genton, Marc G.
  作者单位：State University of New York (SUNY) System; Binghamton University, SUNY; King Abdullah University of Science & Technology
  摘要：We propose a new class of transGaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of ...
• Basis Function Models for Animal Movement AI翻译
  作者：Hooten, Mevin B.; Johnson, Devin S.
  作者单位：Colorado State University System; Colorado State University Fort Collins; United States Department of the Interior; United States Geological Survey; National Oceanic Atmospheric Admin (NOAA) - USA
  摘要：Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-based data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal telemetry data of varying q...
• Evaluating Utility Measurement From Recurrent Marker Processes in the Presence of Competing Terminal Events AI翻译
  作者：Sun, Yifei; Wang, Mei-Cheng
  作者单位：Johns Hopkins University
  摘要：In follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this article, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events, respectively, the recurrent marker process is the trajectory of cumulative cost, which stops to increase after death. In many...
• Density Level Sets: Asymptotics, Inference, and Visualization AI翻译
  作者：Chen, Yen-Chi; Genovese, Christopher R.; Wasserman, Larry
  作者单位：University of Washington; University of Washington Seattle; Carnegie Mellon University
  摘要：We study the plug-in estimator for density level sets under Hausdorff loss. We derive asymptotic theory for this estimator, and based on this theory, we develop two bootstrap confidence regions for level sets. We introduce a new technique for visualizing density level sets, even in multidimensions, which is easy to interpret and efficient to compute. Supplementary materials for this article are available online.
• Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing Comment AI翻译
  作者：Tran, Dustin; Blei, David M.
  作者单位：Columbia University
• A Multi-Resolution Approximation for Massive Spatial Datasets AI翻译
  作者：Katzfuss, Matthias
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of high-resolution observations of spatial fields over large spatial regions. If these datasets can be efficiently exploited, they can provide new insights on a wide variety of issues. However, traditional spatial-statistical techniques such as kriging are not computationally feasible for big datasets. We propose a multi-resolution approximation (M-RA) of Gaussian processes observed at irreg...
• Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation AI翻译
  作者：Steingrimsson, Jon Arni; Strawderman, Robert L.
  作者单位：Johns Hopkins University; University of Rochester
  摘要：This article considers linear regression with missing covariates and a right censored outcome. We first consider a general two-phase outcome sampling design, where full covariate information is only ascertained for subjects in phase two and sampling occurs under an independent Bernoulli sampling scheme with known subject-specific sampling probabilities that depend on phase one information (e.g., survival time, failure status and covariates). The semiparametric information bound is derived for ...