The Annals of Applied Statistics

最新文章

• VARIABLE SELECTION FOR BART: AN APPLICATION TO GENE REGULATION AI翻译
  作者：Bleich, Justin; Kapelner, Adam; George, Edward I.; Jensen, Shane T.
  作者单位：University of Pennsylvania
  摘要：We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat...
• DEGRADATION-BASED RESIDUAL LIFE PREDICTION UNDER DIFFERENT ENVIRONMENTS AI翻译
  作者：Zhou, Rensheng; Serban, Nicoleta; Gebraeel, Nagi
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Degradation modeling has traditionally relied on historical signals to estimate the behavior of the underlying degradation process. Many models assume that these historical signals are acquired under the same environmental conditions and can be observed along the entire lifespan of a component. In this paper, we relax these assumptions and present a more general statistical framework for modeling degradation signals that may have been collected under different types of environmental conditions...
• RECONSTRUCTING EVOLVING SIGNALLING NETWORKS BY HIDDEN MARKOV NESTED EFFECTS MODELS AI翻译
  作者：Wang, Xin; Yuan, Ke; Hellmayr, Christoph; Liu, Wei; Markowetz, Florian
  作者单位：CRUK Cambridge Institute; Cancer Research UK; University of Cambridge
  摘要：Inferring time-varying networks is important to understand the development and evolution of interactions over time. However, the vast majority of currently used models assume direct measurements of node states, which are often difficult to obtain, especially in fields like cell biology, where perturbation experiments often only provide indirect information of network structure. Here we propose hidden Markov nested effects models (HM-NEMs) to model the evolving network by a Markov chain on a st...
• REDUCED-RANK SPATIO-TEMPORAL MODELING OF AIR POLLUTION CONCENTRATIONS IN THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS AND AIR POLLUTION AI翻译
  作者：Olives, Casey; Sheppard, Lianne; Lindstrom, Johan; Sampson, Paul D.; Kaufman, Joel D.; Szpiro, Adam A.
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Lund University; University of Washington; University of Washington Seattle
  摘要：There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captu...
• INTERPOLATION OF NONSTATIONARY HIGH FREQUENCY SPATIAL-TEMPORAL TEMPERATURE DATA AI翻译
  作者：Guinness, Joseph; Stein, Michael L.
  作者单位：University of Chicago; University of Chicago
  摘要：The Atmospheric Radiation Measurement program is a U.S. Department of Energy project that collects meteorological observations at several locations around the world in order to study how weather processes affect global climate change. As one of its initiatives, it operates a set of fixed but irregularly-spaced monitoring facilities in the Southern Great Plains region of the U. S. We describe methods for interpolating temperature records from these fixed facilities to locations at which no obse...
• SPARSE LEAST TRIMMED SQUARES REGRESSION FOR ANALYZING HIGH-DIMENSIONAL LARGE DATA SETS AI翻译
  作者：Alfons, Andreas; Croux, Christophe; Gelper, Sarah
  作者单位：KU Leuven; Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
  摘要：Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse model estimation. A robust and sparse estimator is introduced by adding an L-1 penalty on the coefficient estimates to the well-known least trimmed squares (LTS) estimator. The breakdown point of this sp...
• BAYESIAN SPLINE METHOD FOR ASSESSING EXTREME LOADS ON WIND TURBINES AI翻译
  作者：Lee, Giwhyun; Byon, Eunshin; Ntaimo, Lewis; Ding, Yu
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Michigan System; University of Michigan
  摘要：This study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level imposed on a turbine structure that the turbine would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic si...
• BAYESIAN NONPARAMETRIC HIERARCHICAL MODELING FOR MULTIPLE MEMBERSHIP DATA IN GROUPED ATTENDANCE INTERVENTIONS AI翻译
  作者：Savitsky, Terrance D.; Paddock, Susan M.
  作者单位：RAND Corporation
  摘要：We develop a dependent Dirichlet process (DDP) model for repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each client through a sequence of elements which overlap with those of other clients on different occasions. Our interest concentrates on study designs for which the overlaps of sequences occur for clients who receive an intervention in a shared or grouped fashion whose memberships may change over multiple tr...
• LEARNING A NONLINEAR DYNAMICAL SYSTEM MODEL OF GENE REGULATION: A PERTURBED STEADY-STATE APPROACH AI翻译
  作者：Meister, Arwen; Li, Ye Henry; Choi, Bokyung; Wong, Wing Hung
  作者单位：Stanford University
  摘要：Biological structure and function depend on complex regulatory interactions between many genes. A wealth of gene expression data is available from high-throughput genome-wide measurement technologies, but effective gene regulatory network inference methods are still needed. Model-based methods founded on quantitative descriptions of gene regulation are among the most promising, but many such methods rely on simple, local models or on ad hoc inference approaches lacking experimental interpretab...
• MODEL-BASED CLUSTERING OF LARGE NETWORKS AI翻译
  作者：Vu, Duy Q.; Hunter, David R.; Schweinberger, Michael
  作者单位：University of Melbourne; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Rice University
  摘要：We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data s...