The Annals of Applied Statistics

最新文章

• COMMON AND INDIVIDUAL STRUCTURE OF BRAIN NETWORKS AI翻译
  作者：Wang, Lu; Zhang, Zhengwu; Dunson, David
  作者单位：Central South University; University of Rochester; Duke University
  摘要：This article focuses on the problem of studying shared-and individual-specific structure in replicated networks or graph-valued data. In particular, the observed data consist of n graphs, G(i), i = 1,..., n, with each graph consisting of a collection of edges between V nodes. In brain connectomics, the graph for an individual corresponds to a set of interconnections among brain regions. Such data can be organized as a V x V binary adjacency matrix A(i) for each i, with ones indicating an edge ...
• GRAPHICAL MODELS FOR ZERO-INFLATED SINGLE CELL GENE EXPRESSION AI翻译
  作者：McDavid, Andrew; Gottardo, Raphael; Simon, Noah; Drton, Mathias
  作者单位：University of Rochester; University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Copenhagen
  摘要：Bulk gene expression experiments relied on aggregations of thousands of cells to measure the average expression in an organism. Advances in microfluidic and droplet sequencing now permit expression profiling in single cells. This study of cell-to-cell variation reveals that individual cells lack detectable expression of transcripts that appear abundant on a population level, giving rise to zero-inflated expression patterns. To infer gene coregulatory networks from such data, we propose a multi...
• PREDICTION MODELS FOR NETWORK-LINKED DATA AI翻译
  作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
  作者单位：University of Virginia; University of Michigan System; University of Michigan
  摘要：Prediction algorithms typically assume the training data are independent samples, but in many modern applications samples come from individuals connected by a network. For example, in adolescent health studies of risktaking behaviors, information on the subjects' social network is often available and plays an important role through network cohesion, the empirically observed phenomenon of friends behaving similarly. Taking cohesion into account in prediction models should allow us to improve th...
• A GENERAL THEORY FOR PREFERENTIAL SAMPLING IN ENVIRONMENTAL NETWORKS AI翻译
  作者：Watson, Joe; Zidek, James, V; Shaddick, Gavin
  作者单位：University of British Columbia; University of Exeter
  摘要：This paper presents a general model framework for detecting the preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a site-selection process that considers where and when sites are placed to measure the process. The environmental process may be spatial, temporal or spatio-temporal in nature. By sharing random effects between the two processes, the joi...
• BAYESIAN SEMIPARAMETRIC JOINT REGRESSION ANALYSIS OF RECURRENT ADVERSE EVENTS AND SURVIVAL IN ESOPHAGEAL CANCER PATIENTS AI翻译
  作者：Lee, Juhee; Thall, Peter F.; Lin, Steven H.
  作者单位：University of California System; University of California Santa Cruz; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
  摘要：We propose a Bayesian semiparametric joint regression model for a recurrent event process and survival time. Assuming independent latent subject frailties, we define marginal models for the recurrent event process intensity and survival distribution as functions of the subject's frailty and baseline covariates. A robust Bayesian model, called Joint-DP, is obtained by assuming a Dirichlet process for the frailty distribution. We present a simulation study that compares posterior estimates under...
• A SIMPLE, CONSISTENT ESTIMATOR OF SNP HERITABILITY FROM GENOME-WIDE ASSOCIATION STUDIES AI翻译
  作者：Schwartzman, Armin; Schork, Andrew J.; Zablocki, Rong; Thompson, Wesley K.
  作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：Analysis of genome-wide association studies (GWAS) is characterized by a large number of univariate regressions where a quantitative trait is regressed on hundreds of thousands to millions of single-nucleotide polymorphism (SNP) allele counts, one at a time. This article proposes an estimator of the SNP heritability of the trait, defined here as the fraction of the variance of the trait explained by the SNPs in the study. The proposed GWAS heritability (GWASH) estimator is easy to compute, hig...
• A HIERARCHICAL MULTIVARIATE SPATIO-TEMPORAL MODEL FOR CLUSTERED CLIMATE DATA WITH ANNUAL CYCLES AI翻译
  作者：Mastrantonio, Gianluca; Lasinio, Giovanna Jona; Pollice, Alessio; Capotorti, Giulia; Teodonio, Lorenzo; Genova, Giulio; Blasi, Carlo
  作者单位：Polytechnic University of Turin; Universita degli Studi di Bari Aldo Moro
  摘要：We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response...
• BAYESIAN INDICATOR VARIABLE SELECTION TO INCORPORATE HIERARCHICAL OVERLAPPING GROUP STRUCTURE IN MULTI-OMICS APPLICATIONS AI翻译
  作者：Zhu, Li; Huo, Zhiguang; Ma, Tianzhou; Oesterreich, Steffi; Tseng, George C.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; State University System of Florida; University of Florida; University System of Maryland; University of Maryland College Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：Variable selection is a pervasive problem in modern high-dimensional data analysis where the number of features often exceeds the sample size (a.k.a. small-n-large-p problem). Incorporation of group structure knowledge to improve variable selection has been widely studied. Here, we consider prior knowledge of a hierarchical overlapping group structure to improve variable selection in regression setting. In genomics applications, for instance, a biological pathway contains tens to hundreds of g...
• 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...
• COHERENCE-BASED TIME SERIES CLUSTERING FOR STATISTICAL INFERENCE AND VISUALIZATION OF BRAIN CONNECTIVITY AI翻译
  作者：Euan, Carolina; Sun, Ying; Ombao, Hernando
  作者单位：King Abdullah University of Science & Technology
  摘要：We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display a high level of coordination as measured by cluster-coherence. While the most common approach to measure dependence between clusters is through pairs of single time series, our method proposes cluster coherence which measures dependence between pairs of whole clusters rather than between single elements. Th...