3期

最新文章

• NETWORK CLASSIFICATION WITH APPLICATIONS TO BRAIN CONNECTOMICS AI翻译
  作者：Relion, Jesus D. Arroyo; Kessler, Daniel; Levina, Elizaveta; Taylor, Stephan F.
  作者单位：Johns Hopkins University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has sh...
• IDENTIFYING AND ESTIMATING PRINCIPAL CAUSAL EFFECTS IN A MULTI-SITE TRIAL OF EARLY COLLEGE HIGH SCHOOLS AI翻译
  作者：Yuan, Lo-Hua; Feller, Avi; Miratrix, Luke W.
  作者单位：Airbnb; University of California System; University of California Berkeley; Harvard University
  摘要：Randomized trials are often conducted with separate randomizations across multiple sites such as schools, voting districts, or hospitals. These sites can differ in important ways, including the site's implementation quality, local conditions, and the composition of individuals. An important question in practice is whether-and under what assumptions-researchers can leverage this cross-site variation to learn more about the intervention. We address these questions in the principal stratification...
• DISTRIBUTIONAL REGRESSION FORESTS FOR PROBABILISTIC PRECIPITATION FORECASTING IN COMPLEX TERRAIN AI翻译
  作者：Schlosser, Lisa; Hothorn, Torsten; Stauffer, Reto; Zeileis, Achim
  作者单位：University of Innsbruck; Swiss School of Public Health (SSPH+); University of Zurich
  摘要：To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over the last decade there has been increasing interest in distributional regression approaches modeling all parameters including location, scale and shape. Notably, so-called nonhomogeneous Gaussian regres...
• SEQUENTIAL DECISION MODEL FOR INFERENCE AND PREDICTION ON NONUNIFORM HYPERGRAPHS WITH APPLICATION TO KNOT MATCHING FROM COMPUTATIONAL FORESTRY AI翻译
  作者：Jun, Seong-Hwan; Wong, Samuel W. K.; Zidek, James, V; Bouchard-Cote, Alexandre
  作者单位：University of British Columbia; University of Waterloo
  摘要：In this paper, we consider the knot-matching problem arising in computational forestry. The knot-matching problem is an important problem that needs to be solved to advance the state of the art in automatic strength prediction of lumber. We show that this problem can be formulated as a quadripartite matching problem and develop a sequential decision model that admits efficient parameter estimation along with a sequential Monte Carlo sampler on graph matching that can be utilized for rapid samp...
• APPROXIMATE INFERENCE FOR CONSTRUCTING ASTRONOMICAL CATALOGS FROM IMAGES AI翻译
  作者：Regier, Jeffrey; Miller, Andrew C.; Schlegel, David; Adams, Ryan P.; McAuliffe, Jon D.; Prabhat
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Columbia University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Princeton University
  摘要：We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at qu...
• BAYESIAN METHODS FOR MULTIPLE MEDIATORS: RELATING PRINCIPAL STRATIFICATION AND CAUSAL MEDIATION IN THE ANALYSIS OF POWER PLANT EMISSION CONTROLS AI翻译
  作者：Kim, Chanmin; Daniels, Michael J.; Hogan, Joseph W.; Choirat, Christine; Zigler, Corwin M.
  作者单位：Boston University; State University System of Florida; University of Florida; Brown University; University of Texas System; University of Texas Austin
  摘要：Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution and human health, many of these relationships have never been empirically verified. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effec...
• RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA AI翻译
  作者：Zhang, Youyi; Morris, Jeffrey S.; Aerry, Shivali Narang; Rao, Arvind U. K.; Baladandayuthapani, Veerabhadran
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Johns Hopkins University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, where-in our goals are to simultaneously identify genomic and radiomic, that is, radiology-based imaging ma...
• OBLIQUE RANDOM SURVIVAL FORESTS AI翻译
  作者：Jaeger, Byron C.; Long, D. Leann; Long, Dustin M.; Sims, Mario; Szychowski, Jeff M.; Min, Yuan-, I; Mcclure, Leslie A.; Howard, George; Simon, Noah
  作者单位：University of Alabama System; University of Alabama Birmingham; University of Mississippi Medical Center; University of Mississippi; Drexel University; University of Washington; University of Washington Seattle
  摘要：We introduce and evaluate the oblique random survival forest (ORSF). The ORSF is an ensemble method for right-censored survival data that uses linear combinations of input variables to recursively partition a set of training data. Regularized Cox proportional hazard models are used to identify linear combinations of input variables in each recursive partitioning step. Benchmark results using simulated and real data indicate that the ORSF's predicted risk function has high prognostic value in c...
• IMPUTATION AND POST-SELECTION INFERENCE IN MODELS WITH MISSING DATA: AN APPLICATION TO COLORECTAL CANCER SURVEILLANCE GUIDELINES AI翻译
  作者：Liu, Lin; Qiu, Yuqi; Natarajan, Loki; Messer, Karen
  作者单位：University of California System; University of California San Diego
  摘要：It is common to encounter missing data among the potential predictor variables in the setting of model selection. For example, in a recent study we attempted to improve the US guidelines for risk stratification after screening colonoscopy (Cancer Causes Control 27 (2016) 1175-1185), with the aim to help reduce both overuse and underuse of follow-on surveillance colonoscopy. The goal was to incorporate selected additional informative variables into a neoplasia risk-prediction model, going beyon...