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作者：Li, Tianxi; Le, Can M.
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of California System; University of California Davis
摘要：Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network's underlying connection pattern given a single and noisy instantiation. While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications. Consequently, network modeling based on these methods either suf...
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作者：Gallagher, Ian; Jones, Andrew; Bertiger, Anna; Priebe, Carey E.; Rubin-Delanchy, Patrick
作者单位：University of Bristol; Microsoft; Johns Hopkins University
摘要：When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results. To formalize this idea, we consider the asymptotic behavior of spectral embedding for different edge-weight representations, under a generic low rank model. We measure the quality of different embeddings-which can be on entirely different scales-by how easy it is to distinguish communities, in an information-theoretical sense. For common types of weighted graphs...
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作者：Xu, Maoran; Zhou, Hua; Hu, Yujie; Duan, Leo L.
作者单位：Duke University; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; State University System of Florida; University of Florida; State University System of Florida; University of Florida
摘要：In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recovery under an unknown low rank, etc. Despite the ease of obtaining a point estimate via optimization, it is much more challenging to quantify their uncertainty. In the Bayesian framework, a major difficulty is that if assigning the prior associated with a p-dimensional measure, then there is zero posterior probability...
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作者：Bortolotti, Teresa; Peli, Riccardo; Lanzano, Giovanni; Sgobba, Sara; Menafoglio, Alessandra
作者单位：Polytechnic University of Milan; Istituto Nazionale Geofisica e Vulcanologia (INGV)
摘要：Motivated by the crucial implications of Ground Motion Models in terms of seismic hazard analysis and civil protection planning, this work extends a scalar Ground Motion Model for Italy to the framework of Functional Data Analysis. The inherent characteristic of seismic data to be incomplete over the observation domain of oscillation periods entails embedding the analysis in the context of partially observed functional data and performing data reconstruction. This work proposes a novel methodo...
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作者：Ki, Caleb; Terhorst, Jonathan
作者单位：University of Michigan System; University of Michigan
摘要：In statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent var...
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作者：Rao, J. Sunil; Li, Mengying; Jiang, Jiming
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Miami; University of California System; University of California Davis
摘要：In many practical problems, there is interest in the estimation of mixed effect projections for new data that are outside the range of the training data. Examples include predicting extreme small area means for rare populations or making treatment decisions for patients who do not fit typical risk profiles. Standard methods have long been known to struggle with such problems since the training data may not provide enough information about potential model changes for these new data values (extr...
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作者：Zheng, Jiajing; Wu, Jiaxi; D'Amour, Alexander; Franks, Alexander
作者单位：University of California System; University of California Santa Barbara; Alphabet Inc.; Google Incorporated
摘要：In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity a...
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作者：Astfalck, Lachlan; Williamson, Daniel; Gandy, Niall; Gregoire, Lauren; Ivanovic, Ruza
作者单位：University of Leeds; University of Exeter; Alan Turing Institute; University of Western Australia
摘要：Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. These boundary conditions are typically fixed using available reconstructions in climate modeling studies; however, in reality they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We devel...
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作者：Fang, Guanhua; Xu, Ganggang; Xu, Haochen; Zhu, Xuening; Guan, Yongtao
作者单位：Fudan University; University of Miami; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; Fudan University
摘要：In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Hawkes process (GNHP) model whose network structure is observed and fixed. In particular, we introduce a latent group structure among individuals to account for the heterogeneous user-specific characteristics. A maximum likelihood approach is proposed to simultaneously cluster individuals in the network and estimate mod...
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作者：Luo, Zhao Tang; Sang, Huiyan; Mallick, Bani
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：There has been a long-standing challenge in developing locally stationary Gaussian process models concerning how to obtain flexible partitions and make predictions near boundaries. In this work, we develop a new class of locally stationary stochastic processes, where local partitions are modeled by a soft partition process via predictive random spanning trees that leads to highly flexible spatially contiguous subregion shapes. This valid nonstationary process model knits together local models ...
