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最新文章

• A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS AI翻译
  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...
• MULTIVARIATE SPATIAL MAPPING OF SOIL WATER HOLDING CAPACITY WITH SPATIALLY VARYING CROSS-CORRELATIONS AI翻译
  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...
• BAYESIAN NONHOMOGENEOUS MARKOV MODELS VIA POLYA-GAMMA DATA AUGMENTATION WITH APPLICATIONS TO RAINFALL MODELING AI翻译
  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 ...
• INFERENCE FOR SOCIAL NETWORK MODELS FROM EGOCENTRICALLY SAMPLED DATA, WITH APPLICATION TO UNDERSTANDING PERSISTENT RACIAL DISPARITIES IN HIV PREVALENCE IN THE US AI翻译
  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 ...
• ASSESSING DIFFERENCES IN LEGISLATORS' REVEALED PREFERENCES: A CASE STUDY ON THE 107TH US SENATE AI翻译
  作者：Lofland, Chelsea L.; Rodriguez, Abel; Moser, Scott
  作者单位：University of California System; University of California Santa Cruz; University of Nottingham
  摘要：Roll call data are widely used to assess legislators' preferences and ideology, as well as test theories of legislative behavior. In particular, roll call data is often used to determine whether the revealed preferences of legislators are affected by outside forces such as party pressure, minority status or procedural rules. This paper describes a Bayesian hierarchical model that extends existing spatial voting models to test sharp hypotheses about differences in preferences using posterior pr...
• STATIC AND ROVING SENSOR DATA FUSION FOR SPATIO-TEMPORAL HAZARD MAPPING WITH APPLICATION TO OCCUPATIONAL EXPOSURE ASSESSMENT AI翻译
  作者：Ludwig, Guilherme; Chu, Tingjin; Zhu, Jun; Wang, Haonan; Koehler, Kirsten
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Renmin University of China; University of Wisconsin System; University of Wisconsin Madison; Colorado State University System; Colorado State University Fort Collins; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the ...
• MODELLING INDIVIDUAL MIGRATION PATTERNS USING A BAYESIAN NONPARAMETRIC APPROACH FOR CAPTURE-RECAPTURE DATA AI翻译
  作者：Matechou, Eleni; Caron, Francois
  作者单位：University of Kent; University of Oxford
  摘要：We present a Bayesian nonparametric approach for modelling wildlife migration patterns using capture-recapture (CR) data. Arrival times of individuals are modelled in continuous time and assumed to be drawn from a Poisson process with unknown intensity function, which is modelled via a flexible nonparametric mixture model. The proposed CR framework allows us to estimate the following: (i) the total number of individuals that arrived at the site, (ii) their times of arrival and departure, and h...
• FORECASTING SEASONAL INFLUENZA WITH A STATE-SPACE SIR MODEL AI翻译
  作者：Osthus, Dave; Hickmann, Kyle S.; Caragea, Petruta C.; Higdon, Dave; Del Valle, Sara Y.
  作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory; Iowa State University; Virginia Polytechnic Institute & State University
  摘要：Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recu...
• COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS AI翻译
  作者：Keller, Joshua P.; Drton, Mathias; Larson, Timothy; Kaufman, Joel D.; Sandler, Dale P.; Szpiro, Adam A.
  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 of Washington; University of Washington Seattle; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
  摘要：Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, w...