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作者：Li, Kendrick Qijun; Shi, Xu; Miao, Wang; Tchetgen, Eric Tchetgen
作者单位：University of Michigan System; University of Michigan; Peking University; University of Pennsylvania
摘要：The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In a TND study, individuals who experience symptoms and seek care are recruited and tested for the infectious disease which defines cases and controls. Despite TND's potential to reduce unobserved differences in healthcare seeking behavior (HSB) between va...
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作者：Ye, Zi; Harrar, Solomon W.
作者单位：Lehigh University; University of Kentucky; University of Kentucky
摘要：Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In some studies, participants are first classified as having or not of the characteristic of interest by diagnostic tools, but such classifiers may not be perfectly accurate. The impact of diagnostic misclassification in statistical inference has been recently investigated in parametric model contexts and shown to introduce severe bias i...
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作者：Guerrier, Stephane; Kuzmics, Christoph; Victoria-Feser, Maria-Pia
作者单位：University of Geneva; University of Geneva; University of Graz; University of Bologna
摘要：Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population. These case count data obviously suffer from participation bias, and for prevalence estimation, these data are typically discarded in favor of infection surveys, or possibly also completed with auxiliary information. One exception is the series of infection surveys recorded by the Statistics Austria Federal Institute to study the prevalence of COVID-19 in Austria in April, May, and Nov...
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作者：Goncalves, Flavio B.; Latuszynski, Krzysztof G.; Roberts, Gareth O. O.
作者单位：Universidade Federal de Minas Gerais; University of Warwick
摘要：In this article, we present a novel methodology to perform Bayesian inference for Cox processes in which the intensity function is driven by a diffusion process. The novelty lies in the fact that no discretization error is involved, despite the non-tractability of both the likelihood function and the transition density of the diffusion. The methodology is based on an MCMC algorithm and its exactness is built on retrospective sampling techniques. The efficiency of the methodology is investigate...
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作者：Kurisu, Daisuke; Kato, Kengo; Shao, Xiaofeng
作者单位：University of Tokyo; Cornell University; University of Illinois System; University of Illinois Urbana-Champaign
摘要：In this article, we establish a high-dimensional CLT for the sample mean of p-dimensional spatial data observed over irregularly spaced sampling sites in Rd, allowing the dimension p to be much larger than the sample size n. We adopt a stochastic sampling scheme that can generate irregularly spaced sampling sites in a flexible manner and include both pure increasing domain and mixed increasing domain frameworks. To facilitate statistical inference, we develop the spatially dependent wild boots...
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作者：Mohammadi, Reza
作者单位：University of Amsterdam
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作者：Poisot, Timothee; Davis, Jerry D.
作者单位：Universite de Montreal
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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...
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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...
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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...
