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作者：Hamura, Yasuyuki; Irie, Kaoru; Sugasawa, Shonosuke
作者单位：Kyoto University; University of Tokyo; Keio University
摘要：Count data with zero inflation and large outliers are ubiquitous in many scientific applications. However, posterior analysis under a standard statistical model, such as Poisson or negative binomial distribution, is sensitive to such contamination. This study introduces a novel framework for Bayesian modeling of counts that is robust to both zero inflation and large outliers. In doing so, we introduce rescaled beta distribution and adopt it to absorb undesirable effects from zero and outlying ...
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作者：Bayle, Pierre; Fan, Jianqing; Lou, Zhipeng
作者单位：Princeton University; University of California System; University of California San Diego
摘要：Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals fo...
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作者：Ding, Xiucai; Ma, Rong
作者单位：University of California System; University of California Davis; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute
摘要：Integrative analysis of multiple heterogeneous datasets has arised in many research fields. Existing approaches oftentimes suffer from limited power in capturing nonlinear structures, insufficient account of noisiness and effects of high-dimensionality, lack of adaptivity to signals and sample sizes imbalance, and their results are sometimes difficult to interpret. To address these limitations, we propose a kernel spectral method that achieves joint embeddings of two independently observed hig...
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作者：Iao, Su I.; Zhou, Yidong; Muller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This article addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the absence of linear struc...
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作者：Zhang, Wei; Wang, Fan; Yao, Fang
作者单位：Peking University; Columbia University
摘要：Research on the localization of the genetic basis associated with diseases or traits has been widely conducted in the last few decades. Scan methods have been developed for region-based analysis in whole-genome association studies, helping us better understand how genetics influences human diseases or traits, especially when the aggregated effects of multiple causal variants are present. In this paper, we propose a fast and effective algorithm coupling with high-dimensional test for simultaneo...
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作者：Boulin, Alexis; Di Bernardino, Elena; Laloe, Thomas; Toulemonde, Gwladys
作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Cote d'Azur; Inria; Universite de Montpellier; Centre National de la Recherche Scientifique (CNRS)
摘要：We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random process among clusters. This class of models is identifiable, meaning that there exists a maximal element with a partial order between partitions, allowing for statistical inference. We also present an algorithm depending on a tuning parameter that recovers the c...
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作者：Iannario, Maria; Dasgupta, Nairanjana; Morrison, Jillian; Raton, Boca
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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...
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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...
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作者：Dewaskar, Miheer; Tosh, Christopher; Knoblauch, Jeremias; Dunson, David B.
作者单位：University of New Mexico; Memorial Sloan Kettering Cancer Center; University of London; University College London; Duke University
摘要：Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, there is inevitably some amount of model misspecification: outliers, data contamination or inappropriate parametric assumptions such as Gaussianity mean that most models are at best rough approximations of reality. A significant practical concern is that for certain inferences, even small amounts of model misspecificat...
