2022

最新文章

• Nonparametric Empirical Bayes Prediction AI翻译
  作者：Cressie, Noel
  作者单位：University of Wollongong
• Bayesian Thinking in Biostatistics AI翻译
  作者：Ni, Yang
  作者单位：Texas A&M University System; Texas A&M University College Station
• Risk Projection for Time-to-Event Outcome Leveraging Summary Statistics With Source Individual-Level Data AI翻译
  作者：Zheng, Jiayin; Zheng, Yingye; Hsu, Li
  作者单位：Fred Hutchinson Cancer Center
  摘要：Predicting risks of chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a cohort, there is a great interest to apply the model to other cohorts. Due to potential discrepancy in baseline disease incidences between different cohorts and shifts in patient composition, the risk predicted by the model built in the source cohort often under- or over-estimates the risk in a new cohort. In this article, we assume the relative risks of predic...
• Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares AI翻译
  作者：Guo, Xiao; Cheng, Guang
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Purdue University System; Purdue University
  摘要：Statistical inferences for quadratic functionals of linear regression parameter have found wide applications including signal detection, global testing, inferences of error variance and fraction of variance explained. Classical theory based on ordinary least squares estimator works perfectly in the low-dimensional regime, but fails when the parameter dimension pn grows proportionally to the sample size n. In some cases, its performance is not satisfactory even when n >= 5p(n). The main contrib...
• Estimating a Change Point in a Sequence of Very High-Dimensional Covariance Matrices AI翻译
  作者：Dette, Holger; Pan, Guangming; Yang, Qing
  作者单位：Ruhr University Bochum; Nanyang Technological University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：This article considers the problem of estimating a change point in the covariance matrix in a sequence of high-dimensional vectors, where the dimension is substantially larger than the sample size. A two-stage approach is proposed to efficiently estimate the location of the change point. The first step consists of a reduction of the dimension to identify elements of the covariance matrices corresponding to significant changes. In a second step, we use the components after dimension reduction t...
• Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data AI翻译
  作者：Sang, Hejian; Kim, Jae Kwang; Lee, Danhyang
  作者单位：Alphabet Inc.; Google Incorporated; Iowa State University; University of Alabama System; University of Alabama Tuscaloosa
  摘要：Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation may be subject to bias under model misspecification. In this article, we propose a novel semiparametric fractional imputation (SFI) method using Gaussian mixture models. The proposed method is computationally efficient and leads to...
• The Potential of Factor Analysis for Replication, Generalization, and Integration AI翻译
  作者：De Boeck, Paul; DeKay, Michael L.; Xu, Menglin
  作者单位：University System of Ohio; Ohio State University; University System of Ohio; Ohio State University
• Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs AI翻译
  作者：Shi, Hongjian; Drton, Mathias; Han, Fang
  作者单位：University of Washington; University of Washington Seattle; Technical University of Munich
  摘要：This article investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. Our approach combines distance covariance with the center-outward ranks and signs developed by Marc Hallin and collaborators. In technical terms, the proposed test is consistent and distribution-free in the family of multivariate distributions with nonvanishing (Lebesgue) probability densities. Exploiting the (degen...
• Sensitivity Analysis via the Proportion of Unmeasured Confounding AI翻译
  作者：Bonvini, Matteo; Kennedy, Edward H.
  作者单位：Carnegie Mellon University
  摘要：In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby the sensitivity param...
• Statistical Modeling for Spatio-Temporal Data From Stochastic Convection-Diffusion Processes AI翻译
  作者：Liu, Xiao; Yeo, Kyongmin; Lu, Siyuan
  作者单位：University of Arkansas System; University of Arkansas Fayetteville; International Business Machines (IBM); IBM USA
  摘要：This article proposes a physical-statistical modeling approach for spatio-temporal data arising from a class of stochastic convection-diffusion processes. Such processes are widely found in scientific and engineering applications where fundamental physics imposes critical constraints on how data can be modeled and how models should be interpreted. The idea of spectrum decomposition is employed to approximate a physical spatio-temporal process by the linear combination of spatial basis function...