The Annals of Applied Statistics

最新文章

• COMPOSITE MIXTURE OF LOG-LINEAR MODELS WITH APPLICATION TO PSYCHIATRIC STUDIES AI翻译
  作者：Aliverti, Emanuele; Dunson, David B.
  作者单位：Universita Ca Foscari Venezia; Duke University
  摘要：Psychiatric studies of suicide provide fundamental insights on the evolution of severe psychopathologies, and contribute to the development of early treatment interventions. Our focus is on modelling different traits of psychosis and their interconnections, focusing on a case study on suicide attempt survivors. Such aspects are recorded via multivariate categorical data, involving a large numbers of items for multiple subjects. Current methods for multivariate categorical data-such as penalize...
• BAYESIAN MITIGATION OF SPATIAL COARSENING FOR A HAWKES MODEL APPLIED TO GUNFIRE, WILDFIRE AND VIRAL CONTAGION AI翻译
  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 ...
• SEQUENTIAL MODELING, MONITORING, AND FORECASTING OF STREAMING WEB TRAFFIC DATA AI翻译
  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...
• TESTING FOR DIFFERENTIAL ABUNDANCE IN COMPOSITIONAL COUNTS DATA, WITH APPLICATION TO MICROBIOME STUDIES AI翻译
  作者：Brill, Barak; Amir, Amnon; Heller, Ruth
  作者单位：Tel Aviv University; Tel Aviv University; Chaim Sheba Medical Center
  摘要：Identifying which taxa in our microbiota are associated with traits of in-terest is important for advancing science and health. However, the identifica-tion is challenging because the measured vector of taxa counts (by amplicon sequencing) is compositional, so a change in the abundance of one taxon in the microbiota induces a change in the number of sequenced counts across all taxa. The data are typically sparse, with many zero counts present either due to biological variance or limited sequen...
• KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES AI翻译
  作者：Wilson, Ander; Hsu, Hsiao-Hsien Leon; Chiu, Yueh-Hsiu Mathilda; Wright, Robert O.; Wright, Rosalind J.; Coull, Brent A.
  作者单位：Colorado State University System; Colorado State University Fort Collins; Icahn School of Medicine at Mount Sinai; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation...
• PREDICTION OF HEREDITARY CANCERS USING NEURAL NETWORKS AI翻译
  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...
• TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA AI翻译
  作者：Zhang, Jingru; Merikangas, Kathleen R.; Li, Hongzhe; Shou, Haochang
  作者单位：University of Pennsylvania; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH)
  摘要：Repeated observations have become increasingly common in biomedi-cal research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropri-ately evaluate how the daily distribution of biosignals might differ across disease groups and demographics. Hence, these data could be formulated as multivariate complex object data, such as probabilit...
• BAYESIAN LOCAL FALSE DISCOVERY RATE FOR SPARSE COUNT DATA WITH APPLICATION TO THE DISCOVERY OF HOTSPOTS IN PROTEIN DOMAINS AI翻译
  作者：Gauran, Iris Ivy M.; Park, Junyong; Rattsev, Ilia; Peterson, Thomas A.; Kann, Maricel G.; Park, DoHwan
  作者单位：King Abdullah University of Science & Technology; Seoul National University (SNU); University System of Maryland; University of Maryland College Park; University of California System; University of California San Francisco; University System of Maryland; University of Maryland College Park
  摘要：In cancer research at the molecular level, it is critical to understand which somatic mutations play an important role in the initiation or progression of cancer. Recently, studying cancer somatic variants at the protein domain level is an important area for uncovering functionally related somatic mutations. The main issue is to find the protein domain hotspots which have significantly high frequency of mutations. Multiple testing procedures are commonly used to identify hotspots; however, whe...
• HIGH-RESOLUTION BAYESIAN MAPPING OF LANDSLIDE HAZARD WITH UNOBSERVED TRIGGER EVENT AI翻译
  作者：Opitz, Thomas; Bakka, Haakon; Huser, Raphael; Lombardo, Luigi
  作者单位：INRAE; University of Oslo; King Abdullah University of Science & Technology; University of Twente
  摘要：Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial hierarchical models for point patterns of landslide occurrences using different types of log-Gaussian Cox processes. Starting from a competitive bas...
• BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS AI翻译
  作者：Wang, Guoqing; Datta, Abhirup; Lindquist, Martin A.
  作者单位：Johns Hopkins University
  摘要：Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large interindividual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain sys...