The Annals of Applied Statistics

最新文章

• COMPOSITE SCORES FOR TRANSPLANT CENTER EVALUATION: A NEW INDIVIDUALIZED EMPIRICAL NULL METHOD AI翻译
  作者：Hartman, Nicholas; Messana, Joseph M.; Kang, Jian; Naik, Abhijit S.; Shearon, Tempie H.; He, Kevin
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Risk-adjusted quality measures are used to evaluate healthcare providers with respect to national norms while controlling for factors beyond their control. Existing healthcare provider profiling approaches typically assume that the between-provider variation in these measures is entirely due to meaningful differences in quality of care. However, in practice, much of the betweenprovider variation will be due to trivial fluctuations in healthcare quality or unobservable confounding risk factors....
• A HIGH-DIMENSIONAL APPROACH TO MEASURE CONNECTIVITY IN THE FINANCIAL SECTOR AI翻译
  作者：Basu, Sumanta; Das, Sreyosh; Michailidis, George; Purnanandam, Amiyatosh
  作者单位：Cornell University; State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Michigan System; University of Michigan
  摘要：Data-driven network models to measure systemic risk in the financial sector and identify too-connected-to-fail institutions are becoming increasingly common in financial applications. Existing statistical methods for building such networks either take a pairwise approach of fitting many bivariate models or a system-wide approach of fitting penalized regression models. The former strategy is prone to large false positive selection, while the latter suffers from shrinkage bias and lack of formal...
• LEARNING COMMON STRUCTURES IN A COLLECTION OF NETWORKS. AN APPLICATION TO FOOD WEBS AI翻译
  作者：Chabert-Liddell, Saint-Clair; Barbillonb, Pierre; Donnetc, Sophie
  作者单位：Universite Paris Saclay; INRAE; AgroParisTech
  摘要：Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of net...
• AS TREATED ANALYSES OF CLUSTER RANDOMIZED TRIALS AI翻译
  作者：Fogelson, Ari I. F.; Landsiedel, Kirsten E.; Dufault, Suzanne M.; Jewell, Nicholas P.
  作者单位：University of London; London School of Hygiene & Tropical Medicine; University of California System; University of California Berkeley; University of California System; University of California San Francisco; University of London; London School of Hygiene & Tropical Medicine
  摘要：Test -negative designs have rapidly become an appealing approach to assess disease interventions when randomization is not feasible and specifically used to measure the effectiveness of vaccines in the field (Vaccine 31 (2013) 2165-2168). An innovative extension of the test -negative design was recently used to assess the impact of a mosquito intervention where the intervention was applied at a cluster level with cluster assignment chosen at random, the AWED (applying Wolbachia to eliminate de...
• A BAYESIAN MODEL OF UNDERREPORTING FOR SEXUAL ASSAULT ON COLLEGE CAMPUSES AI翻译
  作者：Bradshaw, Casey; Blei, David M.
  作者单位：Columbia University
  摘要：In an effort to quantify and combat sexual assault, U.S. colleges and universities are required to disclose the number of reported sexual assaults on their campuses each year. However, many instances of sexual assault are never reported to authorities, and consequently, the number of reported assaults does not fully reflect the true total number of assaults that occurred; the reported values could arise from many combinations of reporting rate and true incidence. In this paper we estimate thes...
• BAYESIAN HIDDEN MARKOV MODEL FOR NATURAL HISTORY OF COLORECTAL CANCER: HANDLING MISCLASSIFIED OBSERVATIONS, VARYING OBSERVATION SCHEMES AND UNOBSERVED DATA AI翻译
  作者：Evala, Apeli; Einaevaara, Irpa; Arkeala, Ytti; Ulathinal, Angita
  作者单位：Finnish Cancer Registry; University of Helsinki
  摘要：Statistical modelling of individual-level event history data arising from varying observation schemes is a challenging problem, particularly due to unobserved and possibly misclassified individual states. Commonly used approaches rely on the hidden Markov models (HMM) to incorporate true underlying states. Each approach needs to account for the underlying data generating process and related external information and requires assumptions for estimation. This article develops a Bayesian HMM for n...
• MULTIPLE LATENT CLUSTERING MODEL FOR THE INFERENCE OF RNA LIFE-CYCLE KINETIC RATES FROM SEQUENCING DATA AI翻译
  作者：Mastrantonio, Gianluca; Bibbona, Enrico; Furlan, Mattia
  作者单位：Polytechnic University of Turin; Istituto Italiano di Tecnologia - IIT; Center for Genomic Science IIT
  摘要：We propose a hierarchical Bayesian model to infer RNA synthesis, processing, and degradation rates from time-course RNA sequencing data, based on an ordinary differential equation system that models the RNA life cycle. We parametrize the latent kinetic rates, which rule the system, with a novel functional form and estimate their parameters through three Dirichlet process mixture models. Owing to the complexity of this approach, we are able to simultaneously perform inference, clustering, and m...
• A FRAMEWORK FOR ANALYSING LONGITUDINAL DATA INVOLVING TIME-VARYING COVARIATES AI翻译
  作者：Drikvandi, Reza; Verbeke, Geert; Molenberghs, Geert
  作者单位：Durham University; KU Leuven; Hasselt University
  摘要：Standard models for longitudinal data ignore the stochastic nature of time-varying covariates and their stochastic evolution over time by treating them as fixed variables. There have been recent methods for modelling timevarying covariates; however, those methods cannot be applied to analyse longitudinal data when the longitudinal response and the time-varying covariates for each subject are measured at different time points. Moreover, it is difficult to study the temporal effects of a time-va...
• ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION AI翻译
  作者：Huang, Penghui; Cai, Manqi; Lu, Xinghua; Mckennan, Chris; Wang, Jiebiao
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing method...
• A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL AI翻译
  作者：Chen, Xinyuan; Harhay, Michael O.; Tong, Guangyu; Li, Fan
  作者单位：Mississippi State University; University of Pennsylvania; Yale University
  摘要：Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during ...