2016

最新文章

• Generalized Dynamic Principal Components AI翻译
  作者：Pena, Daniel; Yohai, Victor J.
  摘要：Brillinger defined dynamic principal components (DPC) for time series based on a reconstruction criterion. He gave a very elegant theoretical solution and proposed an estimator which is consistent under stationarity. Here, we propose a new enterally empirical approach to DPC. The main differences with the existing methods mainly Brillinger procedure are (1) the DPC we propose need not be a linear combination of the observations and (2) it can be based on a variety of loss functions including r...
• I-Optimal Design of Mixture Experiments AI翻译
  作者：Goos, Peter; Jones, Bradley; Syafitri, Utami
  作者单位：KU Leuven
  摘要：In mixture experiments, the factors under study are proportions of the-ingredients-of a mixture. The special nature of the factors necessitates specific types of regression models, and specific types of experimental designs. Although mixture experiments usually are intended to predict the response(s) for all possible formulations of the mixture and to identify optimal proportions for each of the ingredients, little research has been done concerning their I-optimal design. This is surprising gi...
• Empirical Likelihood for Right Censored Lifetime Data AI翻译
  作者：He, Shuyuan; Liang, Wei; Shen, Junshan; Yang, Grace
  作者单位：Capital Normal University; Xiamen University; Peking University; University System of Maryland; University of Maryland College Park
  摘要：When the empirical likelihood (EL) of a parameter theta is constructed with right censored data, literature shows that 2 log(empirical likelihood ratio) typically has an asymptotic scaled chi-squared distribution, where the scale parameter is a function of some unknown asymptotic variances. Therefore, the EL construction of confidence intervals for theta requires an additional estimation of the scale parameter. Additional estimation would reduce the coverage accuracy for theta. By using a spec...
• Minimax and Adaptive Estimation of Covariance Operator for Random Variables Observed on a Lattice Graph AI翻译
  作者：Cai, T. Tony; Yuan, Ming
  作者单位：University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison
  摘要：Covariance structure plays an important role in high-dimensional statistical inference. In a range of applications including imaging analysis and fMRI studies, random variables are observed on a lattice graph. In such a setting, it is important to account for the lattice structure when estimating the covariance operator. In this article, we consider both minimax and adaptive estimation of the covariance operator over collections of polynomially decaying and exponentially decaying parameter spa...
• Collective Estimation of Multiple Bivariate Density Functions With Application to Angular-Sampling-Based Protein Loop Modeling AI翻译
  作者：Maadooliat, Mehdi; Zhou, Lan; Najibi, Seyed Morteza; Gao, Xin; Huang, Jianhua Z.
  作者单位：Marquette University; Texas A&M University System; Texas A&M University College Station; Persian Gulf University; King Abdullah University of Science & Technology
  摘要：This article develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating bloc...
• Sparse Approximate Inference for Spatio-Temporal Point Process Models AI翻译
  作者：Cseke, Botond; Zammit-Mangion, Andrew; Heskes, Tom; Sanguinetti, Guido
  作者单位：University of Edinburgh; University of Bristol; Radboud University Nijmegen
  摘要：Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms sca...
• Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets AI翻译
  作者：Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.
  作者单位：University of California System; University of California Los Angeles
  摘要：Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations beconne large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a s...
• Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes AI翻译
  作者：Fox, Eric W.; Short, Martin B.; Schoenberg, Frederic P.; Coronges, Kathryn D.; Bertozzi, Andrea L.
  作者单位：University of California System; University of California Los Angeles
  摘要：We propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accountin...
• The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation AI翻译
  作者：Oates, Chris J.; Papamarkou, Theodore; Girolami, Mark
  作者单位：University of Technology Sydney
  摘要：Approximation of the model evidence is well known to be challenging. One promising approach is based on thermodynamic integration, but a key concern is that the thermodynamic integral can suffer from high variability in many applications. This article considers the reduction of variance that can be achieved by exploiting control variates in this setting. Our methodology applies whenever the gradient of both the log likelihood and the log-prior with respect to the parameters can be efficiently ...
• Functional CAR Models for Large Spatially Correlated Functional Datasets AI翻译
  作者：Zhang, Lin; Baladandayuthapani, Veerabhadran; Zhu, Hongxiao; Baggerly, Keith A.; Majewski, Tadeusz; Czerniak, Bogdan A.; Morris, Jeffrey S.
  作者单位：University of Texas System; UTMD Anderson Cancer Center
  摘要：We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional- response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying ...