4期

最新文章

• MULTILEVEL TIME-SERIES MODELS FOR SMALL AREA ESTIMATION AT DIFFERENT FREQUENCIES AND DOMAIN LEVELS AI翻译
  作者：Boonstra, Harm Jan; Van Den Brakel, Jan
  作者单位：Maastricht University
  摘要：A small area estimation method is developed for repeatedly conducted multipurpose surveys. A multilevel time-series model is proposed that uses direct estimates for the most detailed domains observed at the highest frequency of the repeated survey. A consistent set of estimates at different aggregation levels is then derived by aggregation of the model-based predictions obtained for the most detailed domains observed at the highest frequency. The model borrows strength over time and space via ...
• BAYESIAN BI-CLUSTERING METHODS WITH APPLICATIONS IN COMPUTATIONAL BIOLOGY AI翻译
  作者：Yan, By Han; Wu, Jiexing; LI, Yang; Liu, Jun S.
  作者单位：Harvard University; Alphabet Inc.; Google Incorporated
  摘要：Bi-clustering is a useful approach in analyzing large biological data sets when the observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian approach in tackling bi-clustering problems in moderate to high dimensions and propose three Bayesian bi-clustering models on categorical data which increase in complexities in their modeling of the distributions of features across bi-clusters. Our proposed methods apply to a wide range of scenarios: f...
• PHYLOGENETICALLY INFORMED BAYESIAN TRUNCATED COPULA GRAPHICAL MODELS FOR MICROBIAL ASSOCIATION NETWORKS AI翻译
  作者：Chung, Hee Cheol; Gaynanova, Irina; Ni, Yang
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：Microorganisms play critical roles in host health. The advancement of high-throughput sequencing technology provides opportunities for a deeper understanding of microbial interactions. However, due to the technological limitations of 16S ribosomal RNA sequencing, microbiome data are zero -inflated, and a quantitative comparison of microbial abundances cannot be made across subjects. By leveraging a recent microbiome profiling technique that quantifies 16S ribosomal RNA microbial counts, we pro...
• FUNCTIONAL RANDOM EFFECTS MODELING OF BRAIN SHAPE AND CONNECTIVITY AI翻译
  作者：Lila, Eardi; Aston, John A. D.
  作者单位：University of Washington; University of Washington Seattle; University of Cambridge
  摘要：We present a statistical framework that jointly models brain shape and functional connectivity which are two complex aspects of the brain that have been classically studied independently. We adopt a Riemannian modeling ap-proach to account for the non-Euclidean geometry of the space of shapes and the space of connectivity that constrains trajectories of covariation to be valid statistical estimates. In order to disentangle genetic sources of variabil-ity from those driven by unique environment...
• SEMI-SUPERVISED NONPARAMETRIC BAYESIAN MODELLING OF SPATIAL PROTEOMICS AI翻译
  作者：Crook, By Oliver m.; Lilley, Kathryn s.; Gatto, Laurent; Kirk, Paul D. W.
  作者单位：MRC Biostatistics Unit; University of Cambridge; University of Cambridge
  摘要：Understanding subcellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high-resolution mapping of thousands of proteins to subcellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a nonparametric Bayesian framework, using K-component mixtures of G...
• HIERARCHICAL RESAMPLING FOR BAGGING IN MULTISTUDY PREDICTION WITH APPLICATIONS TO HUMAN NEUROCHEMICAL SENSING AI翻译
  作者：Loewinger, Gabriel; Patil, Prasad; Kishida, Kenneth T.; Parmigiani, Giovanni
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Boston University; Wake Forest University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
  摘要：We propose the study strap ensemble, which combines advantages of two common approaches to fitting prediction models when multiple training datasets (studies) are available: pooling studies and fitting one model vs. averaging predictions from multiple models each fit to individual studies. The study strap ensemble fits models to bootstrapped datasets or pseudo-studies. These are generated by resampling from multiple studies with a hierarchical resampling scheme that generalizes the randomized ...
• MAPPING INTERSTELLAR DUST WITH GAUSSIAN PROCESSES AI翻译
  作者：Miller, Andrew C.; Anderson, Lauren; Leistedt, Boris; Cunningham, John P.; Hogg, David W.; Blei, David M.
  作者单位：Columbia University; Columbia University; New York University; Simons Foundation; Flatiron Institute
  摘要：Interstellar dust corrupts nearly every stellar observation and accounting for it is crucial to measuring physical properties of stars. We model the dust distribution as a spatially varying latent field with a Gaussian process (GP) and develop a likelihood model and inference method that scales to millions of astronomical observations. Modeling interstellar dust is complicated by two factors. The first is integrated observations. The data come from a van-tage point on Earth, and each observati...
• FULL BAYESIAN INFERENCE IN HIDDEN MARKOV MODELS OF PLANT GROWTH AI翻译
  作者：Viaud, Gautier; Chen, Yuting; Cournede, Paul-Henry
  作者单位：Universite Paris Saclay; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory
  摘要：Accurately modeling the growth process of plants in interaction with their environment is important for predicting their biophysical characteris-tics, referred to as phenotype prediction. Most models are described by dis-crete dynamic systems in general state-space representation with important domain-specific characteristics: First, plant model parameters have usually clear functional meanings and may be of genetic origins, thus necessitating a precise estimation. Second, critical growth vari...
• SPATIOTEMPORAL SATELLITE DATA IMPUTATION USING SPARSE FUNCTIONAL DATA ANALYSIS AI翻译
  作者：Zhu, Weicheng; Zhu, Zhengyuan; Dai, Xiangtao
  作者单位：Iowa State University
  摘要：Many scientific applications and signal processing algorithms require complete satellite images. However, missing data in satellite images is very common due to various reasons such as cloud cover and sensor-specific prob-lems. This paper introduces a general spatiotemporal satellite image impu-tation method based on sparse functional data analytic techniques. To han-dle observations consisting of a few longitudinally repeated satellite images that are themselves partially observed and noise-c...
• CAUSAL INFERENCE FOR THE EFFECT OF MOBILITY ON COVID-19 DEATHS AI翻译
  作者：Bonvini, Matteo; Kennedy, Edward H.; Ventura, Valerie; Wasserman, Larry
  作者单位：Carnegie Mellon University
  摘要：In this paper we develop statistical methods for causal inference in epi-demics. Our focus is in estimating the effect of social mobility on deaths in the first year of the Covid-19 pandemic. We propose a marginal structural model motivated by a basic epidemic model. We estimate the counterfactual time series of deaths under interventions on mobility. We conduct several types of sensitivity analyses. We find that the data support the idea that reduced mo-bility causes reduced deaths, but the c...