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作者：Pitkin, James; Manolopoulou, Ioanna; Ross, Gordon
作者单位：Alan Turing Institute; University of London; University College London; University of Edinburgh
摘要：The field of retail analytics has been transformed by the availability of rich data, which can be used to perform tasks such as demand forecasting and inventory management. However, one task which has proved more challenging is the forecasting of demand for products which exhibit very few sales. The sparsity of the resulting data limits the degree to which traditional analytics can be deployed. To combat this, we represent sales data as a structured sparse multivariate point process, which all...
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作者：Chen, Yewen; Chang, Xiaohui; Zhang, Bohai; Huang, Hui
作者单位：University System of Georgia; University of Georgia; Oregon State University; Beijing Normal-Hong Kong Baptist University; Renmin University of China; Renmin University of China
摘要：Numerical air quality models, such as the Community Multiscale Air Quality (CMAQ) system, play a critical role in characterizing pollution levels at fine spatial and temporal scales. The model outputs, however, tend to systematically over- or underestimate the real pollutant concentrations. In this study we propose a Bayesian hierarchical dynamic model to calibrate largescale grid-level CMAQ model outputs using data from other sources, especially point-level observations from sparsely located ...
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作者：Jun, Mikyoung; Cook, Scott
作者单位：University of Houston System; University of Houston; Texas A&M University System; Texas A&M University College Station; Bush School of Government & Public Service
摘要：We develop flexible multivariate spatiotemporal Hawkes process models to analyze patterns of terrorism. Previous applications of point process methods to political violence data mainly utilize temporal Hawkes process models, neglecting spatial variation in these attack patterns. This limits what can be learned from these models, as any effective counter -terrorism strategy requires knowledge on both when and where attacks are likely to occur. Even the existing work on spatiotemporal Hawkes pro...
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作者：Sun, Jiehuan; Basu, Sanjib
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：High-dimensional biomarkers, such as gene expression profiles, are often collected longitudinally to monitor disease progression in clinical studies, where the primary endpoint of interest is often a survival outcome. It is of great interest to study the associations between high-dimensional longitudinal biomarkers and the survival outcome as well as to identify biomarkers related to the survival outcome. Joint models, which have been extensively studied in the past decades, are commonly used ...
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作者：Jiao, Shuhao; Frostig, Ron; Ombao, Hernando
作者单位：City University of Hong Kong; University of California System; University of California Irvine; King Abdullah University of Science & Technology
摘要：Local field potentials (LFPs) are signals that measure electrical activities in localized cortical regions and are collected from multiple tetrodes implanted across a patch on the surface of cortex. Hence, they can be treated as multigroup functional data, where the trajectories collected across temporal epochs from one tetrode are viewed as a group of functions. In many cases multitetrode LFP trajectories contain both global variation patterns (which are shared by most groups, due to signal s...
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作者：Economou, Theodoros; Johnson, Catrina; Dyson, Elizabeth
作者单位：Met Office - UK
摘要：Weather observations are important for a wide range of applications although they do pose statistical challenges, such as missing values, errors, flawed outliers and poor spatial and temporal coverage to name a few. A Bayesian hierarchical spline framework is presented here to deal with such challenges in temperature time series. Motivated by a real-life problem, the approach uses penalised splines, constructed hierarchically, to pool the data, along with a discrete mixture distribution to dea...
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作者：Xu, Tianchen; Chen, Kun; Li, Gen
作者单位：Bristol-Myers Squibb; University of Connecticut; University of Michigan System; University of Michigan
摘要：Multivariate longitudinal data are frequently encountered in practice such as in our motivating longitudinal microbiome study. It is of general interest to associate such high -dimensional, longitudinal measures with some univariate continuous outcome. However, incomplete observations are common in a regular study design, as not all samples are measured at every time point, giving rise to the so-called blockwise missing values. Such missing structure imposes significant challenges for associat...
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作者：Han, Larry; Li, Yige; Niknam, Bijan; Zubizarreta, Jost
作者单位：Northeastern University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School
摘要：Accurate hospital performance measurement is important to both patients and providers but is challenging due to case -mix heterogeneity, differences in treatment guidelines, and data privacy regulations that preclude the sharing of individual patient data. Motivated to overcome these issues in the setting of hospital quality measurement, we develop a federated causal inference framework. We devise a doubly robust estimator of the mean potential outcome in a target population and show that it i...
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作者：Liu, Zihuan; Lee, Cheuk Yin; Zhang, Heping
作者单位：Yale University; Chinese University of Hong Kong
摘要：Neuroimaging studies often involve predicting a scalar outcome from an array of images collectively called tensor. The use of magnetic resonance imaging (MRI) provides a unique opportunity to investigate the structures of the brain. To learn the association between MRI images and human intelligence, we formulate a scalar-on-image quantile regression framework. However, the high dimensionality of the tensor makes estimating the coefficients for all elements computationally challenging. To addre...
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作者：Patel, Ashish; Ditraglia, Francis J.; Zuber, Verena; Burgess, Stephen
作者单位：MRC Biostatistics Unit; University of Cambridge; University of Oxford; Imperial College London
摘要：Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have greater confidence in the instrument validity of only a smaller subset of variants. Nevertheles...
