Journal of the American Statistical Association

最新文章

• Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests AI翻译
  作者：Mohammadi, Reza
  作者单位：University of Amsterdam
• Introduction to Environmental Data Science AI翻译
  作者：Poisot, Timothee; Davis, Jerry D.
  作者单位：Universite de Montreal
• Fitting Latent Non-Gaussian Models Using Variational Bayes and Laplace Approximations AI翻译
  作者：Cabral, Rafael; Bolin, David; Rue, Havard
  作者单位：King Abdullah University of Science & Technology
  摘要：Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to geostatistics, it is easy to find datasets that contain inherently non-Gaussian features, such as sudden jumps or spikes, that adversely affect the inferences and predictions made using an LGM. These datasets require more general latent non-Gaussian models (LnGMs) that can handle automatically these non-Gaussi...
• Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments AI翻译
  作者：Wang, Bingkai; Park, Chan; Small, Dylan S.; Li, Fan
  作者单位：University of Pennsylvania; Yale University; Yale University
  摘要：Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods-generalized estimating equations and linear mixed models-wit...
• Enveloped Huber Regression AI翻译
  作者：Zhou, Le; Cook, R. Dennis; Zou, Hui
  作者单位：Hong Kong Baptist University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Huber regression (HR) is a popular flexible alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least s...
• Smoothness-Penalized Deconvolution (SPeD) of a Density Estimate AI翻译
  作者：Kent, David; Ruppert, David
  作者单位：Cornell University; Cornell University
  摘要：This article addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared mth derivative. Theory for deconvolution has mainly focused on kernel- or wavelet-based techniques, but other methods including spline-based techniques and t...
• Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions AI翻译
  作者：Deuber, David; Li, Jinzhou; Engelke, Sebastian; Maathuis, Marloes H.
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Geneva; Stanford University
  摘要：Causal inference for extreme events has many potential applications in fields such as climate science, medicine, and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however, the most relevant cases relate to extremal quantiles that go beyond the data range. We introduce an es...
• PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection AI翻译
  作者：Huang, Sihan; Sun, Jiajin; Feng, Yang
  作者单位：Columbia University; New York University
  摘要：One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, and various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce a pairwise covariates-adjusted stochastic block model (PCABM...
• Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data AI翻译
  作者：Niu, Yabo; Ni, Yang; Pati, Debdeep; Mallick, Bani K.
  作者单位：University of Houston System; University of Houston; Texas A&M University System; Texas A&M University College Station
  摘要：In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets under-utilized in determining the joint dependency structure. In this article, we consider a Bayesian approach to model undirected graphs underlying heterogeneous multivariate observations with additional assistance from covariates. Building on product partiti...
• Tail Spectral Density Estimation and Its Uncertainty Quantification: Another Look at Tail Dependent Time Series Analysis AI翻译
  作者：Zhang, Ting; Xu, Beibei
  作者单位：University System of Georgia; University of Georgia
  摘要：We consider the estimation and uncertainty quantification of the tail spectral density, which provide a foundation for tail spectral analysis of tail dependent time series. The tail spectral density has a particular focus on serial dependence in the tail, and can reveal dependence information that is otherwise not discoverable by the traditional spectral analysis. Understanding the convergence rate of tail spectral density estimators and finding rigorous ways to quantify their statistical unce...