2014

最新文章

• Bayesian Multiscale Modeling of Closed Curves in Point Clouds AI翻译
  作者：Gu, Kelvin; Pati, Debdeep; Dunson, David B.
  作者单位：Stanford University; State University System of Florida; Florida State University; Duke University
  摘要：Modeling object boundaries based on image or point cloud data is frequently necessary in medical and scientific applications ranging from detecting tumor contours for targeted radiation therapy, to the classification of organisms based on their structural information. In low-contrast images or sparse and noisy point clouds, there is often insufficient data to recover local segments of the boundary in isolation. Thus, it becomes critical to model the entire boundary in the form of a closed curv...
• A Bayesian Nonparametric Modeling Framework for Developmental Toxicity Studies Comment AI翻译
  作者：Politis, Dimitris N.
  作者单位：University of California System; University of California San Diego
• Nonparametric Regression for Spherical Data AI翻译
  作者：Di Marzio, Marco; Panzera, Agnese; Taylor, Charles C.
  作者单位：G d'Annunzio University of Chieti-Pescara; University of Florence; University of Leeds
  摘要：We develop nonparametric smoothing for regression when both the predictor and the response variables are defined on a sphere of whatever dimension. A local polynomial fitting approach is pursued, which retains all the advantages in terms of rate optimality, interpretability, and ease of implementation widely observed in the standard setting. Our estimates have a multi-output nature, meaning that each coordinate is separately estimated, within a scheme of a regression with a linear response. Th...
• Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model-Averaged Causal Effects AI翻译
  作者：Zigler, Corwin Matthew; Dominici, Francesca
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of big data are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model to satisfy the assumptions necessary for estimating average causal effects. Typically, ...
• Sparse Semiparametric Nonlinear Model With Application to Chromatographic Fingerprints Rejoinder AI翻译
  作者：Wierzbicki, Michael R.; Guo, Li-Bing; Du, Qing-Tao; Guo, Wensheng
  作者单位：University of Pennsylvania; Guangdong Pharmaceutical University
• Bayesian Aggregation of Order-Based Rank Data AI翻译
  作者：Deng, Ke; Han, Simeng; Li, Kate J.; Liu, Jun S.
  作者单位：Tsinghua University; Harvard University; Suffolk University
  摘要：Rank aggregation, that is, combining several ranking functions (called base rankers) to get aggregated, usually stronger rankings of a given set of items, is encountered in many disciplines. Most methods in the literature assume that base rankers of interest are equally reliable. It is very common in practice, however, that some rankers are more informative and reliable than others. It is desirable to distinguish high quality base rankers from low quality ones and treat them differently. Some ...
• Sparse Semiparametric Nonlinear Model With Application to Chromatographic Fingerprints Comment AI翻译
  作者：Du, Pang
  作者单位：Virginia Polytechnic Institute & State University
• Efficient R-Estimation of Principal and Common Principal Components AI翻译
  作者：Hallin, Marc; Paindaveine, Davy; Verdebout, Thomas
  作者单位：Universite Libre de Bruxelles; Princeton University; Universite Libre de Bruxelles; Universite de Lille; Inria; Universite de Lille
  摘要：We propose rank-based estimators of principal components, both in the one-sample and, under the assumption of common principal components, in the m-sample cases. Those estimators are obtained via a rank-based version of Le Cam's one-step method, combined with an estimation of cross-information quantities. Under arbitrary elliptical distributions with, in the m-sample case, possibly heterogeneous radial densities, those R-estimators remain root-n consistent and asymptotically normal, while achi...
• Accurate Directional Inference for Vector Parameters in Linear Exponential Families AI翻译
  作者：Davison, A. C.; Fraser, D. A. S.; Reid, N.; Sartori, N.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Toronto; University of Padua
  摘要：We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value is computed using one-dimensional integration. The work simplifies and develops earlier research on directional tests for continuous models and on higher-order inference for discrete models, and the examples include contingency tables and logi...
• Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models AI翻译
  作者：Liu, Ziyue; Cappola, Anne R.; Crofford, Leslie J.; Guo, Wensheng
  作者单位：Indiana University System; Indiana University Indianapolis; Indiana University System; Indiana University Indianapolis; University of Pennsylvania; University of Kentucky; University of Pennsylvania
  摘要：The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this article, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with ...