4期

最新文章

• BAYESIAN MODELING OF INSURANCE CLAIMS FOR HAIL DAMAGE AI翻译
  作者：Miralles, Ophelia; Davison, Anthony c.
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Despite its importance for insurance, there is almost no literature on statistical hail damage modeling. Statistical models for hailstorms exist, though they are generally not open-source, but no study appears to have developed a stochastic hail impact function. In this paper we use hail-related insurance claim data to build a Gaussian line process with extreme marks in order to model both the geographical footprint of a hailstorm and the damage to buildings that hailstones can cause. We build...
• MULTISITE DISEASE ANALYTICS WITH APPLICATIONS TO ESTIMATING COVID-19 UNDETECTED CASES IN CANADA AI翻译
  作者：Parker, Matthew R. P.; Cao, Jiguo; Cowen, Laura l. E.; Elliott, Lloyd T.; Ma, Junling
  作者单位：Simon Fraser University; University of Victoria
  摘要：Even with daily case counts, the true scope of the COVID-19 pandemic in Canada is unknown due to undetected cases. We develop a novel multivalued multivariate time series in the framework of Bayesian hidden Markov modelling techniques. We apply our multisite model to estimate the pandemic scope using publicly available disease count data including detected cases, recoveries among detected cases, and total deaths. These counts are used to estimate the case detection probability, the infection f...
• MODELING URBAN CRIME OCCURRENCES VIA NETWORK REGULARIZED REGRESSION AI翻译
  作者：Upton, Elizabeth; Carvalho, Luis
  作者单位：Williams College; Boston University
  摘要：Analyses of occurrences of residential burglary in urban areas have shown that crime rates are not spatially homogeneous: rates vary across the network of city streets, resulting in some areas being far more susceptible to crime than others. The explanation for why a certain segment of the city experiences high crime may be different than why a neighboring area experiences high crime. Motivated by the importance of understanding spatial patterns such as these, we consider a statistical model o...
• BAYESIAN ROBUST LEARNING IN CHAIN GRAPH MODELS FOR INTEGRATIVE PHARMACOGENOMICS AI翻译
  作者：Chakraborty, Moumita; Baladandayuthapani, Veerabhadran; Bhadra, Anindya; Ha, Min jin
  作者单位：University of Texas System; University of Texas Medical Branch Galveston; University of Michigan System; University of Michigan; Purdue University System; Purdue University; Yonsei University
  摘要：Integrative analysis of multilevel pharmacogenomic data for modeling dependencies across various biological domains is crucial for developing genomic-testing based treatments. Chain graphs characterize conditional dependence structures of such multilevel data where variables are naturally partitioned into multiple ordered layers, consisting of both directed and undirected edges. Existing literature mostly focus on Gaussian chain graphs, which are ill-suited for nonnormal distributions with hea...
• LEARNING BRAIN CONNECTIVITY IN SOCIAL COGNITION WITH DYNAMIC NETWORK REGRESSION AI翻译
  作者：Zhang, Maoyu; Cai, Biao; Dai, Wenlin; Kong, Dehan; Zhao, Hongyu; Zhang, Jingfei
  作者单位：Emory University; City University of Hong Kong; Renmin University of China; University of Toronto; Yale University
  摘要：Dynamic networks have been increasingly used to characterize brain connectivity that varies during resting and task states. In such characterizations a connectivity network is typically measured at each time point for a subject over a common set of nodes representing brain regions, together with rich subject-level information. A common approach to analyzing such data is an edge-based method that models the connectivity between each pair of nodes separately. However, such approach may have limi...
• ASSESSING MARINE MAMMAL ABUNDANCE: A NOVEL DATA FUSION AI翻译
  作者：Schliep, Erin M.; Gelfand, Alan E.; Clark, Christopher W.; Mayo, Charles A.; Mckenna, Brigid; Parks, Susan E.; Yack, Tina M.; Schick, Roberts.
  作者单位：North Carolina State University; Duke University; Cornell University; Syracuse University; Duke University
  摘要：Marine mammals are increasingly vulnerable to human disturbance and climate change. Their diving behavior leads to limited visual access during data collection, making studying the abundance and distribution of marine mammals challenging. In theory, using data from more than one observation modality should lead to better informed predictions of abundance and distribution. With focus on North Atlantic right whales, we consider the fusion of two data sources to inform about their abundance and d...
• DYNAMIC TOPIC LANGUAGE MODEL ON HETEROGENEOUSCHILDREN'S MENTAL HEALTH CLINICAL NOTES AI翻译
  作者：Ye, Hanwen; Moreno, Tatiana; Alpern, Adrianne; Ehwerhemuepha, Louis; Qu, Annie
  作者单位：University of California System; University of California Irvine; Childrens Hospital of Orange County
  摘要：Mental health diseases which affect children's lives and well-beings havereceived increased attention since the COVID-19 pandemic. Analyzing psy-chiatric clinical notes with topic models is critical to evaluating children'smental status over time. However, few topic models are built for longitudinalsettings, and most existing approaches fail to capture temporal trajectoriesfor each document. To address these challenges, we develop a dynamic topicmodel with consistent topics and individualized ...
• MODELS WITH OBSERVATION ERROR AND TEMPORARY EMIGRATION FOR COUNT DATA AI翻译
  作者：Ketwaroo, Fabian r.; Matechou, Eleni; Biddle, Rebecca; Tollington, Simon; DA Silva, Maria l.
  作者单位：Swiss Ornithological Institute; University of Kent; Nottingham Trent University; Universidade Federal do Para
  摘要：Count data at surveyed sites are an important monitoring tool for several species around the world. However, the raw count data are an underestimate of the size of the monitored population at any one time, as individuals can temporarily leave the site (temporary emigration, TE) and because the probability of detection of individuals, even when using the site, is typically much lower than one (observation error). In this paper we develop a novel modelling framework for estimating population siz...
• PREDICTING MILK TRAITS FROM SPECTRAL DATA USING BAYESIAN PROBABILISTIC PARTIAL LEAST SQUARES REGRESSION AI翻译
  作者：Urbas, Szymon; Lovera, Pierre; Daly, Robert; O'Riordan, Alan; Berry, Donagh; Gormley, Isobel Claire
  作者单位：University College Dublin; University College Cork; Teagasc
  摘要：High-dimensional spectral data-routinely generated in dairy production-are used to predict a range of traits in milk products. Partial least squares (PLS) regression is ubiquitously used for these prediction tasks. However, PLS regression is not typically viewed as arising from a probabilistic model, and parameter uncertainty is rarely quantified. Additionally, PLS regression does not easily lend itself to model-based modifications, coherent prediction intervals are not readily available, and ...
• A COPULA MODEL FOR MARKED POINT PROCESS WITH A TERMINAL EVENT: AN APPLICATION IN DYNAMIC PREDICTION OF INSURANCE CLAIMS AI翻译
  作者：Yang, Lu; Shi, Peng; Huang, Shimeng
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
  摘要：Accurate prediction of an insurer's outstanding liabilities is crucial for maintaining the financial health of the insurance sector. We aim to develop a statistical model for insurers to dynamically forecast unpaid losses by leveraging the granular transaction data on individual claims. The liability cash flow from a single insurance claim is determined by an event process that describes the recurrences of payments, a payment process that generates a sequence of payment amounts, and a settleme...