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作者：Monti, Ricardo Pio; Anagnostopoulos, Christoforos; Montana, Giovanni
作者单位：Imperial College London; Imperial College London; Guy's & St Thomas' NHS Foundation Trust; University of London; King's College London
摘要：In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Typically, data is collected across a cohort of subjects and the scientific objectives consist of estimating population and subject-specific connectivity networks. A third objective that is often overlooked involves quantifying inter-subject variability, and thus identifying regions or subnetworks that demonstrate hete...
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作者：Antonelli, Joseph; Schwartz, Joel; Kloog, Itai; Coull, Brent A.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Ben-Gurion University of the Negev
摘要：Fine particulate matter (PM2.5) measured at a given location is a mix of pollution generated locally and pollution traveling long distances in the atmosphere. Therefore, the identification of spatial scales associated with health effects can inform on pollution sources responsible for these effects, resulting in more targeted regulatory policy. Recently, prediction methods that yield high-resolution spatial estimates of PM2.5 exposures allow one to evaluate such scale-specific associations. We...
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作者：Chakrabarti, Deepayan
作者单位：University of Texas System; University of Texas Austin
摘要：Twitter is a popular medium for individuals to gather information and express opinions on topics of interest to them. By understanding who is interested in what topics, we can gauge the public mood, especially during periods of polarization such as elections. However, while the total volume of tweets may be huge, many people tweet rarely, and tweets are short and often noisy. Hence, directly inferring topics from tweets is both complicated and difficult to scale. Instead, the network structure...
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作者：Gao, Jiti; Peng, Bin; Ren, Zhao; Zhang, Xiaohui
作者单位：Monash University; University of Bath; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Exeter
摘要：Obesity has become one of the major public health issues during the last three decades. A considerable number of determinants have been proposed for body mass index (BMI) by a large range of studies from multiple disciplines. In addition, it is well documented that impacts of these determinants are varying across demographic groups. However, little is known about the relative importance of these potential determinants and the varying impacts of all relatively important determinants. Using the ...
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作者：Zhang, Yuping; Ouyang, Zhengqing; Zhao, Hongyu
作者单位：University of Connecticut; University of Connecticut; Jackson Laboratory; University of Connecticut; Yale University
摘要：Recent advances in high-throughput biotechnologies have generated various types of genetic, genomic, epigenetic, transcriptomic and proteomic data across different biological conditions. It is likely that integrating data from diverse experiments may lead to a more unified and global view of biological systems and complex diseases. We present a coherent statistical framework for integrating various types of data from distinct but related biological conditions through graphical models. Specific...
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作者：Messick, Rachel M.; Heaton, Matthew J.; Hansen, Neil
作者单位：Brigham Young University
摘要：Irrigation in agriculture mitigates the adverse effects of drought and improves crop production and yield. Still, water scarcity remains a persistent issue and water resources need to be used responsibly. To improve water use efficiency, precision irrigation is emerging as an approach where farmers can vary the application of water according to within field variation in soil and topographic conditions. As a precursor, methods to characterize spatial variation of soil hydraulic properties are n...
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作者：Kuusela, Mikael; Stark, Philip B.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of California System; University of California Berkeley; University of Chicago
摘要：The high energy physics unfolding problem is an important statistical inverse problem in data analysis at the Large Hadron Collider (LHC) at CERN. The goal of unfolding is to make nonparametric inferences about a particle spectrum from measurements smeared by the finite resolution of the particle detectors. Previous unfolding methods use ad hoc discretization and regularization, resulting in confidence intervals that can have significantly lower coverage than their nominal level. Instead of re...
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作者：Holsclaw, Tracy; Greene, Arthur M.; Robertson, Andrew W.; Smyth, Padhraic
作者单位：University of California System; University of California Irvine; Columbia University
摘要：Discrete-time hiddenMarkov models are a broadly useful class of latentvariable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common in practice to introduce temporal nonhomogeneity into such models by making the transition probabilities dependent on time-varying exogenous input variables via a multinomial logistic parametrization. We extend such models to introduce additional nonhomogeneity into the emission distribution using a ...
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作者：Krivitsky, Pavel N.; Morris, Martina
作者单位：University of Wollongong; University of Washington; University of Washington Seattle
摘要：Egocentric network sampling observes the network of interest from the point of view of a set of sampled actors, who provide information about themselves and anonymized information on their network neighbors. In survey research, this is often the most practical, and sometimes the only, way to observe certain classes of networks, with the sexual networks that underlie HIV transmission being the archetypal case. Although methods exist for recovering some descriptive network features, there is no ...
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作者：Wang, Tao; Zhao, Hongyu
作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Yale University
摘要：Compositional data arise naturally in many practical problems and the analysis of such data presents many statistical challenges, especially in high dimensions. In this article, we consider the problem of subcomposition selection in regression with compositional covariates, where the relationships among the covariates can be represented by a tree with leaf nodes corresponding to covariates. Assuming that the tree structure is available as prior knowledge, we adopt a symmetric version of the li...
