Journal of the American Statistical Association

最新文章

• Optimal Design of Experiments for Implicit Models AI翻译
  作者：Duarte, Belmiro P. M.; Atkinson, Anthony C.; Granjo, Jose F. O.; Oliveira, Nuno M. C.
  作者单位：Instituto Politecnico de Coimbra (IPC); Universidade de Coimbra; Universidade de Coimbra; University of London; London School Economics & Political Science
  摘要：Explicit models representing the response variables as functions of the control variables are standard in virtually all scientific fields. For these models, there is a vast literature on the optimal design of experiments (ODoE) to provide good estimates of the parameters with the use of minimal resources. Contrarily, the ODoE for implicit models is more complex and has not been systematically addressed. Nevertheless, there are practical examples where the models relating the response variables...
• Gaining Outlier Resistance With Progressive Quantiles: Fast Algorithms and Theoretical Studies AI翻译
  作者：She, Yiyuan; Wang, Zhifeng; Shen, Jiahui
  作者单位：State University System of Florida; Florida State University
  摘要：Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this article, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algor...
• Regression Analysis of Asynchronous Longitudinal Functional and Scalar Data AI翻译
  作者：Li, Ting; Li, Tengfei; Zhu, Zhongyi; Zhu, Hongtu
  作者单位：Shanghai University of Finance & Economics; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Fudan University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Many modern large-scale longitudinal neuroimaging studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, have collected/are collecting asynchronous scalar and functional variables that are measured at distinct time points. The analyses of temporally asynchronous functional and scalar variables pose major technical challenges to many existing statistical approaches. We propose a class of generalized functional partial-linear varying-coefficient models to appropriately de...
• Copula Gaussian Graphical Models for Functional Data AI翻译
  作者：Solea, Eftychia; Li, Bing
  作者单位：University of Cyprus; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
  摘要：We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive for large data. We d...
• A Mode-Jumping Algorithm for Bayesian Factor Analysis AI翻译
  作者：Man, Albert Xingyi; Culpepper, Steven Andrew
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Exploratory factor analysis is a dimension-reduction technique commonly used in psychology, finance, genomics, neuroscience, and economics. Advances in computational power have opened the door for fully Bayesian treatments of factor analysis. One open problem is enforcing rotational identifability of the latent factor loadings, as the loadings are not identified from the likelihood without further restrictions. Nonidentifability of the loadings can cause posterior multimodality, which can prod...
• Learning Latent Factors From Diversified Projections and Its Applications to Over-Estimated and Weak Factors AI翻译
  作者：Fan, Jianqing; Liao, Yuan
  作者单位：Princeton University; Rutgers University System; Rutgers University New Brunswick
  摘要：Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weakly serially dependent, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases, it is difficult to analyze the eigenvectors of the data matrix. To address this issue, we propose simple es...
• Restricted Spatial Regression Methods: Implications for Inference AI翻译
  作者：Khan, Kori; Calder, Catherine A.
  作者单位：University System of Ohio; Ohio State University; University of Texas System; University of Texas Austin
  摘要：The issue of spatial confounding between the spatial random effect and the fixed effects in regression analyses has been identified as a concern in the statistical literature. Multiple authors have offered perspectives and potential solutions. In this article, for the areal spatial data setting, we show that many of the methods designed to alleviate spatial confounding can be viewed as special cases of a general class of models. We refer to this class as restricted spatial regression (RSR) mod...
• Regularized Optimal Transport of Covariates and Outcomes in Data Recoding AI翻译
  作者：Gares, Valerie; Omer, Jeremy
  作者单位：Universite de Rennes; Institut National des Sciences Appliquees de Rennes; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
  摘要：When databases are constructed from heterogeneous sources, it is not unusual that different encodings are used for the same outcome. In such case, it is necessary to recode the outcome variable before merging two databases. The method proposed for the recoding is an application of optimal transportation where we search for a bijective mapping between the distributions of such variable in two databases. In this article, we build upon the work by Gares et al., where they transport the distributi...
• Semiparametric Regression with R: Jaroslaw Harezlak, David Ruppert, and Matt P. Wand, New York, NY: Springer, 2018, xi+331 pp., $149.99(P), $109.00(e-Book), ISBN 978-1-4939-8851-8(P)/978-1-4939-8853-2(e-Book). AI翻译
  作者：Wang, Zixiao; Feng, Yi; Liu, Lin
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Shanghai Jiao Tong University; Shanghai Jiao Tong University
• Measuring Housing Vitality from Multi-Source Big Data and Machine Learning AI翻译
  作者：Zhou, Yang; Xue, Lirong; Shi, Zhengyu; Wu, Libo; Fan, Jianqing
  作者单位：Fudan University; Princeton University; Fudan University; Fudan University
  摘要：Measuring timely high-resolution socioeconomic outcomes is critical for policymaking and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity. This article demonstrates an adaptive way to measure the time trend and spatial distribution of housing vitality (number of occupied houses) with the help of multiple easily accessible datasets: energy, nig...