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作者：Yarger, Drew; Stoev, Stilian; Hsing, Tailen
作者单位：University of Michigan System; University of Michigan
摘要：The Argo data is a modern oceanography dataset that provides unprecedented global coverage of temperature and salinity measurements in the upper 2000 meters of depth of the ocean. We study the Argo data from the perspective of functional data analysis (FDA). We develop spatiotemporal functional kriging methodology for mean and covariance estimation to predict temperature and salinity at a fixed location as a smooth function of depth. By combining tools from FDA and spatial statistics, includin...
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作者：Santos-Fernandez, Edgar; Denti, Francesco; Mengersen, Kerrie; Mira, Antonietta
作者单位：Queensland University of Technology (QUT); University of California System; University of California Irvine; Universita della Svizzera Italiana
摘要：Following the introduction of high-resolution player tracking technology, a new range of statistical analysis has emerged in sports, specifically in basketball. However, such high-dimensional data are often challenging for statistical inference and decision making. In this article we employ a state-of-the-art Bayesian mixture model that allows the estimation of heterogeneous intrinsic dimension (ID) within a dataset, and we propose some theoretical enhancements. Informally, the ID can be seen ...
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作者：Schumacher, Austin E.; McCormicK, Tyler H.; Wakefield, Jon; Chu, Yue; Perin, Jamie; Villavicencio, Francisco; Simon, Noah; Liu, Li
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University System of Ohio; Ohio State University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：In order to implement disease-specific interventions in young age groups, policy makers in low- and middle-income countries require timely and accurate estimates of age- and cause-specific child mortality. High-quality data is not available in settings where these interventions are most needed, but there is a push to create sample registration systems that collect detailed mortality information. Current methods that estimate mortality from this data employ multistage frameworks without rigorou...
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作者：Fisher, Thomas J.; Zhang, Jing; Colegate, Stephen P.; Vanni, Michael J.
作者单位：University System of Ohio; Miami University; University System of Ohio; University of Cincinnati; University System of Ohio; Miami University
摘要：We propose a framework to detect and model shifts in a time series of continuous proportions, that is, a vector of proportions measuring the parts of a whole. By reparameterizing the shape of a Dirichlet distribution, we can model the location and scale separately through generalized linear models. A hidden Markov model allows the coefficients of the generalized linear models to change, thus allowing for the time series to undergo multiple regimes. This framework allows a practitioner to adequ...
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作者：Holbrook, Andrew J.; Ji, Xiang; Suchard, Marc A.
作者单位：University of California System; University of California Los Angeles; Tulane University; University of California System; University of California Los Angeles
摘要：Self-exciting spatiotemporal Hawkes processes have found increasing use in the study of large-scale public health threats, ranging from gun violence and earthquakes to wildfires and viral contagion. Whereas many such applications feature locational uncertainty, that is, the exact spatial positions of individual events are unknown, most Hawkes model analyses to date have ignored spatial coarsening present in the data. Three particular 21st century public health crises-urban gun violence, rural ...
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作者：Irie, Kaoru; Glynn, Chris; Aktekin, Tevfik
作者单位：University of Tokyo; University System Of New Hampshire; University of New Hampshire
摘要：In this paper we introduce strategies for modeling, monitoring, and forecasting sequential web traffic data using flows from the Fox News website. In our analysis we consider a family of Poisson-gamma state space (PGSS) models that can accurately quantify the uncertainty exhibited by web traffic data, can provide fast sequential monitoring and prediction mechanisms for high frequency time intervals, and are computationally feasible when structural breaks are present. As such, we extend the fam...
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作者：Guan, Zoe; Parmigiani, Giovanni; Braun, Danielle; Trippa, Lorenzo
作者单位：Memorial Sloan Kettering Cancer Center; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions, based on knowledge of cancer susceptibility genes. These models are widely used in clinical practice to help identify high-risk individuals. Mendelian models leverage the entire family history, but they rely on many assumptions about cancer susceptibility genes that are either unrealistic or challenging to validate, due to low mutation preval...
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作者：Russo, Massimiliano; Singer, Burton H.; Dunson, David B.
作者单位：Harvard University; Harvard Medical School; State University System of Florida; University of Florida; State University System of Florida; University of Florida; Duke University
摘要：Characterizing the shared memberships of individuals in a classification scheme poses severe interpretability issues, even when using a moderate number of classes (say four). Mixed membership models quantify this phenomenon, but they typically focus on goodness-of-fit more than on interpretable inference. To achieve a good numerical fit, these models may, in fact, require many extreme profiles, making the results difficult to interpret. We introduce a new class of multivariate mixed membership...
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作者：Zhou, Ling; Sun, Shiquan; Fu, Haoda; Song, Peter X-K
作者单位：Southwestern University of Finance & Economics - China; Xi'an Jiaotong University; Eli Lilly; University of Michigan System; University of Michigan
摘要：The emerging field of precision medicine is transforming statistical analysis from the classical paradigm of population-average treatment effects into that of personal treatment effects. This new scientific mission has called for adequate statistical methods to assess heterogeneous covariate effects in regression analysis. This paper focuses on a subgroup analysis that consists of two primary analytic tasks: identification of treatment effect subgroups and individual group memberships, and sta...
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作者：Zhang, Baqun; Zhang, Min
作者单位：Shanghai University of Finance & Economics; University of Michigan System; University of Michigan
摘要：When treatment effect heterogeneity exists, identifying the subgroup of patients who would benefit from an active treatment relative to a control is an important question. This article focuses on subgroup identification in the presence of a large dimensional set of covariates, with the number of covariates possibly greater than the sample size. We approach this problem from the perspective of optimal treatment decision rules and propose methods that can simultaneously estimate the treatment de...
