Journal of the American Statistical Association

最新文章

• Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling AI翻译
  作者：Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua
  作者单位：University of Rochester; State University of New York (SUNY) System; University at Albany, SUNY; George Washington University
  摘要：The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group least ...
• Joint Estimation of the Mean and Error Distribution in Generalized Linear Models AI翻译
  作者：Huang, Alan
  作者单位：University of Technology Sydney
  摘要：This article introduces a semiparametric extension of generalized linear models that is based on a full probability model, but does not require specification of an error distribution or variance function for the data. The approach involves treating the error distribution as an infinite-dimensional parameter, which is then estimated simultaneously with the mean-model parameters using a maximum empirical likelihood approach. The resulting estimators are shown to be consistent and jointly asympto...
• Modeling Space and Space-Time Directional Data Using Projected Gaussian Processes AI翻译
  作者：Wang, Fangpo; Gelfand, Alan E.
  作者单位：Adobe Systems Inc.; Duke University
  摘要：Directional data naturally arise in many scientific fields, such as oceanography (wave direction), meteorology (wind direction), and biology (animal movement direction). Our contribution is to develop a fully model-based approach to capture structured spatial dependence for modeling directional data at different spatial locations. We build a projected Gaussian spatial process, induced from an inline bivariate Gaussian spatial process. We discuss the properties of the projected Gaussian process...
• Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening AI翻译
  作者：Shao, Xiaofeng; Zhang, Jingsi
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Northwestern University
  摘要：In this article, we propose a new metric, the so-called martingale difference correlation, to measure the departure of conditional mean independence between a scalar response variable V and a vector predictor variable U. Our metric is a natural extension of distance correlation proposed by Szekely, Rizzo, and Bahirov, which is used to measure the dependence between V and U. The martingale difference correlation and its empirical counterpart inherit a number of desirable features of distance co...
• Fused Estimators of the Central Subspace in Sufficient Dimension Reduction AI翻译
  作者：Cook, R. Dennis; Zhang, Xin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：When studying the regression of a univariate variable Y on a vector x of predictors, most existing sufficient dimension-reduction (SDR) methods require the construction of slices of Y to estimate moments of the conditional distribution of X given Y. But there is no widely accepted method for choosing the number of slices, while a poorly chosen slicing scheme may produce miserable results. We propose a novel and easily implemented fusing method that can mitigate the problem of choosing a slicin...
• A Bayesian Nonparametric Modeling Framework for Developmental Toxicity Studies Comment AI翻译
  作者：Dunson, David B.
  作者单位：Duke University
• A Bayesian Nonparametric Modeling Framework for Developmental Toxicity Studies AI翻译
  作者：Fronczyk, Kassandra; Kottas, Athanasios
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of California System; University of California Santa Cruz
  摘要：We develop a Bayesian nonparametric mixture modeling framework for replicated count responses in dose-response settings. We explore this methodology for modeling and risk assessment in developmental toxicity studies, where the primary objective is to determine the relationship between the level of exposure to a toxic chemical and the probability of a physiological or biochemical response, or death. Data from these experiments typically involve features that cannot be captured by standard param...
• A Bayesian Nonparametric Modeling Framework for Developmental Toxicity Studies Comment AI翻译
  作者：Gelman, Andrew; Vehtari, Aki
  作者单位：Columbia University; Aalto University
• Interaction Screening for Ultrahigh-Dimensional Data AI翻译
  作者：Hao, Ning; Zhang, Hao Helen
  作者单位：University of Arizona
  摘要：In ultrahigh-dimensional data analysis, it is extremely challenging to identify important interaction effects, and a top concern in practice is computational feasibility. For a dataset with n observations and p predictors, the augmented design matrix including all linear and order-2 terms is of size n x (p(2) + 3p)/2. When p is large, say more than tens of hundreds, the number of interactions is enormous and beyond the capacity of standard machines and software tools for storage and analysis. ...
• Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies AI翻译
  作者：Hansen, Ben B.; Rosenbaum, Paul R.; Small, Dylan S.
  作者单位：University of Michigan System; University of Michigan; University of Pennsylvania
  摘要：Clustered treatment assignment occurs when individuals are grouped into clusters prior to treatment and whole clusters, not individuals, are assigned to treatment or control. In randomized trials, clustered assignments may be required because the treatment must be applied to all children in a classroom, or to all patients at a clinic, or to all radio listeners in the same media market. The most common cluster randomized design pairs 2S clusters into S pairs based on similar pretreatment covari...