1期

最新文章

• SPATIOTEMPORAL WILDFIRE MODELING THROUGH POINT PROCESSES WITH MODERATE AND EXTREME MARKS (vol 17, pg 560, 2023) AI翻译
  作者：Koh, Jonathan; Pimont, Francois; Dupuy, Jean-Luc; Opitz, Thomas
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Bern; INRAE; INRAE
• MODELING THE VISIBILITY DISTRIBUTION FOR RESPONDENT-DRIVEN SAMPLING WITH APPLICATION TO POPULATION SIZE ESTIMATION AI翻译
  作者：Mclaughlin, Katherine R.; Johnston, Lisa G.; Jakupi, Xhevat; Gexha-bunjaku, Dafina; Deva, Edona; Handcock, Mark S.
  作者单位：Oregon State University; University of California System; University of California Los Angeles
  摘要：Respondent -driven sampling (RDS) is used throughout the world to estimate prevalence and population size for hidden populations. Although RDS is an effective method for enrolling people from key populations in studies, it relies on a partially unknown sampling mechanism, and thus each individual's inclusion probability is unknown. Current estimators for population prevalence, population size, and other outcomes rely on a participant's network size (degree) to approximate their inclusion proba...
• BAYESIAN MODEL-BASED CLUSTERING FOR POPULATIONS OF NETWORK DATA AI翻译
  作者：Mantziou, Anastasia; Lunagomez, Simon; Mitra, Robin
  作者单位：Alan Turing Institute; Instituto Tecnologico Autonomo de Mexico; University of London; University College London
  摘要：There is increasing appetite for analysing populations of network data due to the fast-growing body of applications demanding such methods. While methods exist to provide readily interpretable summaries of heterogeneous network populations, these are often descriptive or ad hoc, lacking any formal justification. In contrast, principled analysis methods often provide results difficult to relate back to the applied problem of interest. Motivated by two complementary applied examples, we develop ...
• TENSOR MIXTURE DISCRIMINANT ANALYSIS WITH APPLICATIONS TO SENSOR ARRAY DATA ANALYSIS AI翻译
  作者：Hou, Xuesong; Mai, Qing; Zou, Hui
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; Florida State University
  摘要：Sensor arrays are often used to identify chemicals by measuring properly chosen chemical interactions. Machine learning techniques are of vital importance to accurately recognize a chemical based on the sensor array measurements. However, sensor array data often take the form of matrices (i.e, two-way tensors), and the concentration levels may have a complex impact on the measurements. Hence, existing linear and/or vector classification methods may be inadequate for sensor array data. In this ...
• CHANGE POINT DETECTION IN DYNAMIC GAUSSIAN GRAPHICAL MODELS: THE IMPACT OF COVID-19 PANDEMIC ON THE US STOCK MARKET AI翻译
  作者：Franzolini, Beatrice; Beskos, Alexandros; De Iorio, Maria; Koziell, Warrick Poklewski; Grzeszkiewicz, Karolina
  作者单位：Bocconi University; University of London; University College London; National University of Singapore; Yale NUS College
  摘要：Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID19 pandemic. In this work we are interested in capturing abrupt changes over time in the conditional dependence across U.S. industry ...
• NONNEGATIVE TENSOR COMPLETION FOR DYNAMIC COUNTERFACTUAL PREDICTION ON COVID-19 PANDEMIC AI翻译
  作者：Zhen, Yaoming; Wang, Junhui
  作者单位：City University of Hong Kong; Chinese University of Hong Kong
  摘要：The COVID-19 pandemic has been a worldwide health crisis for the past three years, casting unprecedented challenges for policymakers in different countries and regions. While one country or region can only implement one social mobility restriction policy at a given time, it is of great interest for policy makers to decide whether to elevate or deelevate the restriction policy from time to time. This article proposes a novel nonnegative tensor completion method to predict the potential counterf...
• INFERRING CHANGES TO THE GLOBAL CARBON CYCLE WITH WOMBAT V2.0, A HIERARCHICAL FLUX-INVERSION FRAMEWORK AI翻译
  作者：Bertolacci, Michael; Zammit-Mangion, Andrew; Schuh, Andrew; Bukosa, Beata; Fisher, Jenny A.; Cao, Yi; Kaushik, Aleya; Cressie, Noel
  作者单位：University of Wollongong; Colorado State University System; Colorado State University Fort Collins; National Institute of Water & Atmospheric Research (NIWA) - New Zealand; University of Wollongong; University of Colorado System; University of Colorado Boulder
  摘要：The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO2) and other important greenhouse gases are changing in response to human influences. These changes need to be quantified to understand climate change and its impacts, but this is difficult to do because natural fluxes occur over large spatial and temporal scales and cannot be directly observed. Flux inversion is a technique that estimates the spatiotemporal distribution of a gas' fluxes using observations of the gas' ...
• BAYESIAN PROJECTIONS OF TOTAL FERTILITY RATE CONDITIONAL ON THE UNITED NATIONS SUSTAINABLE DEVELOPMENT GOALS AI翻译
  作者：Liu, Daphne H.; Raftery, Adrian E.
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：Women's educational attainment and contraceptive prevalence are two mechanisms identified as having an accelerating effect on fertility decline and that can be directly impacted by policy. Quantifying the potential accelerating effect of education and family planning policies on fertility decline in a probabilistic way is of interest to policymakers, particularly in highfertility countries. We propose a conditional Bayesian hierarchical model for projecting fertility, given education and famil...
• RZIMM-SCRNA: A REGULARIZED ZERO-INFLATED MIXTURE MODEL FRAMEWORK FOR SINGLE-CELL RNA-SEQ DATA AI翻译
  作者：Mi, Xinlei; Bekerman, William; Rustgi, Anil K.; Sims, Peter A.; Canoll, Peter D.; Hu, Jianhua
  作者单位：Columbia University
  摘要：Applications of single -cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However, unique characteristics of scRNA-seq data, including large dimensionality, high dropout rates, and possibly batch effects, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein we propose a u...
• TESTING FOR THE CAUSAL MEDIATION EFFECTS OF MULTIPLE MEDIATORS USING THE KERNEL MACHINE DIFFERENCE METHOD IN GENOME-WIDE EPIGENETIC STUDIES AI翻译
  作者：Shen, Jincheng; Schwartz, Joel; Baccarelli, Andrea A.; Lin, Xihong
  作者单位：Utah System of Higher Education; University of Utah; Harvard University; Harvard T.H. Chan School of Public Health; Columbia University; Columbia University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：The rapid growth of high -throughput genomic and epigenomic data enables exploration of biological mechanisms underlying diseases causing processes beyond traditional association studies. Using the causal mediation analysis framework, we develop the kernel machine difference (KMD) method, which provides a testing procedure for detecting the mediation effects of a set of mediators, for example, the DNA methylation probes within a region or a gene. Our method extends the difference method in sin...