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作者：Xu, Ganggang; Zhang, Jingfei; Li, Yehua; Guan, Yongtao
作者单位：University of Miami; University of California System; University of California Riverside
摘要：Mark-point dependence plays a critical role in research problems that can be fitted into the general framework of marked point processes. In this work, we focus on adjusting for mark-point dependence when estimating the mean and covariance functions of the mark process, given independent replicates of the marked point process. We assume that the mark process is a Gaussian process and the point process is a log-Gaussian Cox process, where the mark-point dependence is generated through the depen...
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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：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...
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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...
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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...
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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...
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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...
