Journal of the American Statistical Association

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• Saha and Ramdas' Discussion of Poisson-FOCuS by Ward, Dilillo, Eckley & Fearnhead AI翻译
  作者：Saha, Aytijhya; Ramdas, Aaditya
  作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Carnegie Mellon University
• Bayesian Nonparametrics for Causal Inference and Missing Data AI翻译
  作者：Hahn, P. Richard; Daniels, Michael J.; Linero, Antonio; Roy, Jason
  作者单位：Arizona State University; Arizona State University-Tempe
• Quantification of Vaccine Waning as a Challenge Effect AI翻译
  作者：Janvin, Matias; Stensrud, Mats J.
  作者单位：University of Oslo; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Knowing whether vaccine protection wanes over time is important for health policy and drug development. However, quantifying waning effects is difficult. A simple contrast of vaccine efficacy at two different times compares different populations of individuals: those who were uninfected at the first time versus those who remain uninfected until the second time. Thus, the contrast of vaccine efficacy at early and late times can not be interpreted as a causal effect. We propose to quantify vacci...
• Tyranny-of-the-Minority Regression Adjustment in Randomized Experiments AI翻译
  作者：Lu, Xin; Liu, Hanzhong
  作者单位：Tsinghua University
  摘要：Regression adjustment is widely used in the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This article reexamines a weighted regression adjustment method termed tyranny-of-the-minority (ToM), wherein units in the minority group are given greater weights. We demonstrate that ToM regression adjustment is more robust than Lin's regression adjustment with treatment-covariate interactions, even though these two regression adjustment methods are asy...
• Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels AI翻译
  作者：Agterberg, Joshua; Lubberts, Zachary; Arroyo, Jesus
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia; Texas A&M University System; Texas A&M University College Station
  摘要：Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across...
• Cutting Feedback in Misspecified Copula Models AI翻译
  作者：Smith, Michael Stanley; Yu, Weichang; Nott, David J.; Frazier, David T.
  作者单位：University of Melbourne; University of Melbourne; National University of Singapore; Monash University
  摘要：In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by cutting feedback. Cutting feedback limits the influence of potentially misspecified modules in posterior inference. We consider two types of cuts. The first limits the influence of a misspecified copula on inference for the marginals, which is a Bayesian analogue of the popular In...
• Unlocking Retrospective Prevalent Information in EHRs-A Revisit to the Pairwise Pseudolikelihood AI翻译
  作者：Keret, Nir; Gorfine, Malka
  作者单位：Tel Aviv University
  摘要：Electronic health records offer abundant data on various diseases and health conditions, enabling researchers to explore the relationship between disease onset age and underlying risk factors. Unlike mortality data, the event of interest is nonterminal, hence, individuals can retrospectively report their disease-onset-age upon recruitment to the study. These individuals, diagnosed with the disease before entering the study, are termed prevalent. The ascertainment imposes a left truncation cond...
• Statistical Inference for Networks of High-Dimensional Point Processes AI翻译
  作者：Wang, Xu; Kolar, Mladen; Shojaie, Ali
  作者单位：University of Washington; University of Washington Seattle; University of Chicago
  摘要：Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes processes. The key ingred...
• Testing Elliptical Models in High Dimensions AI翻译
  作者：Wang, Siyao; Lopes, Miles E.
  作者单位：University of California System; University of California Davis
  摘要：Due to the broad applications of elliptical models, there is a long line of research on goodness-of-fit tests for empirically validating them. However, the existing literature on this topic is generally confined to low-dimensional settings, and to the best of our knowledge, there are no established goodness-of-fit tests for elliptical models that are supported by theoretical guarantees in high dimensions. In this article, we propose a new goodness-of-fit test for this problem, and our main res...
• Modeling Hypergraphs with Diversity and Heterogeneous Popularity AI翻译
  作者：Yu, Xianshi; Zhu, Ji
  作者单位：University of Michigan System; University of Michigan
  摘要：While relations among individuals make an important part of data with scientific and business interests, existing statistical modeling of relational data has mainly been focusing on dyadic relations, that is, those between two individuals. This article addresses the less studied, though commonly encountered, polyadic relations that can involve more than two individuals. In particular, we propose a new latent space model for hypergraphs using determinantal point processes, which is driven by th...