Journal of the American Statistical Association

最新文章

• Modeling and Learning on High-Dimensional Matrix-Variate Sequences AI翻译
  作者：Zhang, Xu; Liu, Catherine C.; Guo, Jianhua; Yuen, K. C.; Welsh, A. H.
  作者单位：South China Normal University; Hong Kong Polytechnic University; Beijing Technology & Business University; University of Hong Kong; Australian National University
  摘要：We propose a new matrix factor model, named RaDFaM, which is strictly derived from the general rank decomposition and assumes a high-dimensional vector factor model structure for each basis vector. RaDFaM contributes a novel class of low-rank latent structures that trade off between signal intensity and dimension reduction from a tensor subspace perspective. Based on the intrinsic separable covariance structure of RaDFaM, for a collection of matrix-valued observations, we derive a new class of...
• Sobolev Calibration of Imperfect Computer Models AI翻译
  作者：Zhang, Qingwen; Wang, Wenjia
  作者单位：Hong Kong University of Science & Technology; Hong Kong University of Science & Technology (Guangzhou); Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
  摘要：Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. This work develops a new calibration method for imperfect computer models, Sobolev calibration, which can rule out calibration parameters that generate overfitting calibrated functions. We prove that the Sobolev calibration enjoys desired theoretical properties including fast convergence rate, asymptotic normality and semipa...
• Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes AI翻译
  作者：Legrand, Juliette; Naveau, Philippe; Oesting, Marco
  作者单位：Universite de Bretagne Occidentale; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); University of Stuttgart; University of Stuttgart
  摘要：Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this article, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the to...
• Analysis of Variance of Tensor Product Reproducing Kernel Hilbert Spaces on Metric Spaces AI翻译
  作者：Wang, Zhanfeng; Pan, Rui; Wang, Xueqin; Wang, Yuedong
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of California System; University of California Santa Barbara
  摘要：Many methods have been developed to analyze complex data, such as non-Euclidean shape, network, and manifold data. However, there is a lack of methods for studying interactions among complex data. In this article, we first propose a novel kernel function for a metric space and construct its associated reproducing kernel Hilbert space. The new nonstationary kernel function provides a flexible and powerful tool for learning complex structures in non-Euclidean data. We then construct an analysis ...
• Zigzag Path Connects Two Monte Carlo Samplers: Hamiltonian Counterpart to a Piecewise Deterministic Markov Process AI翻译
  作者：Nishimura, Akihiko; Zhang, Zhenyu; Suchard, Marc A.
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
  摘要：Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The p...
• Functional Data Analysis with R AI翻译
  作者：Kokoszka, Piotr S.
  作者单位：Colorado State University System; Colorado State University Fort Collins
• A Sparse Beta Regression Model for Network Analysis AI翻译
  作者：Stein, Stefan; Feng, Rui; Leng, Chenlei
  作者单位：University of Warwick
  摘要：For statistical analysis of network data, the beta -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the beta -model, this article proposes the Sparse beta -Regression Model (S beta RM) that unites two research themes developed recently in modeling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized likelihoo...
• Test and Measure for Partial Mean Dependence Based on Machine Learning Methods AI翻译
  作者：Cai, Leheng; Guo, Xu; Zhong, Wei
  作者单位：Tsinghua University; Tsinghua University; Beijing Normal University; Xiamen University; Xiamen University
  摘要：It is of importance to investigate the significance of a subset of covariates W for the response Y given covariates Z in regression modeling. To this end, we propose a significance test for the partial mean independence problem based on machine learning methods and data splitting. The test statistic converges to the standard Chi-squared distribution under the null hypothesis while it converges to a normal distribution under the fixed alternative hypothesis. Power enhancement and algorithm stab...
• Generalized Data Thinning Using Sufficient Statistics AI翻译
  作者：Dharamshi, Ameer; Neufeld, Anna; Motwani, Keshav; Gao, Lucy L.; Witten, Daniela; Bien, Jacob
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of British Columbia; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Southern California
  摘要：Our goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural exponential families, X can be thinned into independent random variables X-(1),& mldr;,X-(K) , such that X=& sum;X-K(k=1)(k) . These independent random variables can then be used for various model validation and inference tasks, including in contexts where tradi...
• Testing the Number of Common Factors by Bootstrapped Sample Covariance Matrix in High-Dimensional Factor Models AI翻译
  作者：Yu, Long; Zhao, Peng; Zhou, Wang
  作者单位：Shanghai University of Finance & Economics; Jiangsu Normal University; Jiangsu Normal University; National University of Singapore
  摘要：This article studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions for the top eigenvalues of bootstrapped sample covariance matrix under mild conditions. After bootstrap, the spiked eigenvalues which are driven by common factors will converge weakly to Gaussian limits after proper scaling and centralization. However, the largest non-spiked eigenvalue is mainly det...