2期

最新文章

• Regression models on Riemannian symmetric spaces AI翻译
  作者：Cornea, Emil; Zhu, Hongtu; Kim, Peter; Ibrahim, Joseph G.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Guelph
  摘要：The paper develops a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arise frequently in medical imaging, surface modelling and computer vision, among many other fields. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distri...
• Testing against a linear regression model using ideas from shape-restricted estimation AI翻译
  作者：Sen, Bodhisattva; Meyer, Mary
  作者单位：Columbia University; Colorado State University System; Colorado State University Fort Collins
  摘要：A formal likelihood ratio hypothesis test for the validity of a parametric regression function is proposed, using a large dimensional, non-parametric double-cone alternative. For example, the test against a constant function uses the alternative of increasing or decreasing regression functions, and the test against a linear function uses the convex or concave alternative. The test proposed is exact and unbiased and the critical value is easily computed. The power of the test increases to 1 as ...
• A Bayesian information criterion for singular models AI翻译
  作者：Drton, Mathias; Plummer, Martyn
  作者单位：University of Washington; University of Washington Seattle; World Health Organization; International Agency for Research on Cancer (IARC)
  摘要：We consider approximate Bayesian model choice for model selection problems that involve models whose Fisher information matrices may fail to be invertible along other competing submodels. Such singular models do not obey the regularity conditions underlying the derivation of Schwarz's Bayesian information criterion BIC and the penalty structure in BIC generally does not reflect the frequentist large sample behaviour of the marginal likelihood. Although large sample theory for the marginal like...
• High dimensional semiparametric latent graphical model for mixed data AI翻译
  作者：Fan, Jianqing; Liu, Han; Ning, Yang; Zou, Hui
  作者单位：Princeton University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent variables that satisfy the Gaussian copula distribution. The goal is to infer the conditional independence relationship between the latent random variables, based on the observed mixed data. Our work has two main contributions: we propose a un...
• Probabilistic multi-resolution scanning for two-sample differences AI翻译
  作者：Soriano, Jacopo; Ma, Li
  作者单位：Duke University
  摘要：We propose a multi-resolution scanning approach to identifying two-sample differences. Windows of multiple scales are constructed through nested dyadic partitioning on the sample space and a hypothesis regarding the two-sample difference is defined on each window. Instead of testing the hypotheses on different windows independently, we adopt a joint graphical model, namely a Markov tree, on the null or alternative states of these hypotheses to incorporate spatial correlation across windows. Th...
• Estimation of extreme depth-based quantile regions AI翻译
  作者：He, Yi; Einmahl, John H. J.
  作者单位：Tilburg University; Tilburg University
  摘要：Consider the extreme quantile region induced by the half-space depth function HD of the form Q={xRd:HD(x,P)}, such that PQ=p for a given, very small p>0. Since this involves extrapolation outside the data cloud, this region can hardly be estimated through a fully non-parametric procedure. Using extreme value theory we construct a natural semiparametric estimator of this quantile region and prove a refined consistency result. A simulation study clearly demonstrates the good performance of our e...
• A frequentist approach to computer model calibration AI翻译
  作者：Wong, Raymond K. W.; Storlie, Curtis B.; Lee, Thomas C. M.
  作者单位：Iowa State University; United States Department of Energy (DOE); Los Alamos National Laboratory; University of California System; University of California Davis
  摘要：The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable par...
• Oracally efficient estimation and consistent model selection for auto-regressive moving average time series with trend AI翻译
  作者：Shao, Qin; Yang, Lijian
  作者单位：Soochow University - China; University System of Ohio; University of Toledo; Tsinghua University
  摘要：Most time series that are encountered in practice contain non-zero trend, yet textbook approaches to time series analysis are typically focused on zero-mean stationary auto-regressive moving average (ARMA) processes. Trend is often estimated by ad hoc methods and subtracted from time series, and the residuals are used as the true ARMA noise for data analysis and inference, including parameter estimation, lag selection and prediction. We propose a theoretically justified two-step method to anal...
• Compound random measures and their use in Bayesian non-parametrics AI翻译
  作者：Griffin, Jim E.; Leisen, Fabrizio
  作者单位：University of Kent
  摘要：A new class of dependent random measures which we call compound random measures is proposed and the use of normalized versions of these random measures as priors in Bayesian non-parametric mixture models is considered. Their tractability allows the properties of both compound random measures and normalized compound random measures to be derived. In particular, we show how compound random measures can be constructed with gamma, sigma-stable and generalized gamma process marginals. We also deriv...
• Confidence intervals and regions for the lasso by using stochastic variational inequality techniques in optimization AI翻译
  作者：Lu, Shu; Liu, Yufeng; Yin, Liang; Zhang, Kai
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：Sparse regression techniques have been popular in recent years because of their ability in handling high dimensional data with built-in variable selection. The lasso is perhaps one of the most well-known examples. Despite intensive work in this direction, how to provide valid inference for sparse regularized methods remains a challenging statistical problem. We take a unique point of view of this problem and propose to make use of stochastic variational inequality techniques in optimization to...