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作者：Das, Debraj; Chatterjee, Arindam; Lahiri, S. N.
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Statistical Institute; Indian Statistical Institute Delhi; Washington University (WUSTL)
摘要：This article develops methodology for higher order accurate two-sided Bootstrap confidence intervals (CIs) in high dimensional penalized regression models using the Bootstrap. We consider a large class of penalized regression methods that satisfy the Oracle property of Fan and Li and a stronger variant of it, called the Strong Oracle property. While second order accuracy of the Bootstrap is known for both classes, it is typically not sufficient to guarantee better accuracy of two-sided Bootstr...
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作者：Bhattacharjee, Satarupa; Mueller, Hans-Georg
作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis
摘要：Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without either global linear or local linear (Riemannian) structure. In such settings the classical additive error model and distributional assumptions are unattainable. Due to the rapid advancement of technology, longitudinal data containing complex random objects, such as covariance matrices, data on Riemannian manifolds, and pro...
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作者：Zhang, Haoran; Wang, Junhui
作者单位：Southern University of Science & Technology; Chinese University of Hong Kong
摘要：Longitudinal networks consist of sequences of temporal edges among multiple nodes, where the temporal edges are observed in real-time. They have become ubiquitous with the rise of online social platforms and e-commerce, but largely under-investigated in the literature. In this article, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring sparse networks so as to...
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作者：Yan, Shunxing; Yao, Fang; Zhou, Hang
作者单位：Peking University; University of California System; University of California Davis
摘要：Nonparametric mean function regression with repeated measurements serves as a cornerstone for many statistical branches, such as longitudinal/panel/functional data analysis. In this work, we investigate this problem using fully connected deep neural network (DNN) estimators with flexible shapes. A novel theoretical framework allowing arbitrary sampling frequency is established by adopting empirical process techniques to tackle clustered dependence. We then consider the DNN estimators for Holde...
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作者：Englert, Jacob R.; Ebelt, Stefanie T.; Chang, Howard H.
作者单位：Emory University; Rollins School Public Health; Emory University
摘要：Epidemiological approaches for examining human health responses to environmental exposures in observational studies often control for confounding by implementing clever matching schemes and using statistical methods based on conditional likelihood. Nonparametric regression models have surged in popularity in recent years as a tool for estimating individual-level heterogeneous effects, which provide a more detailed picture of the exposure-response relationship but can also be aggregated to obta...
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作者：Ghosal, Rahul; Ghosh, Sujit K.; Schrack, Jennifer A.; Zipunnikov, Vadim
作者单位：University of South Carolina System; University of South Carolina Columbia; North Carolina State University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：Modern clinical and epidemiological studies widely employ wearables to record parallel streams of real-time data on human physiology and behavior. With recent advances in distributional data analysis, these high-frequency data are now often treated as distributional observations resulting in novel regression settings. Motivated by these modeling setups, we develop a distributional outcome regression via quantile functions (DORQF) that expands existing literature with three key contributions: (...
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作者：Hong, Shizhe; Li, Weiming; Liu, Qiang; Zhang, Yangchun
作者单位：Shanghai University of Finance & Economics; Shanghai University
摘要：The R-2 statistic and its classic adjusted version, say R-& lowast;2 , tend to overestimate the multiple correlation coefficient when dealing with multivariate data that exhibit heavy tails and tail dependence. This can result in an incorrect significance of correlation in high-dimensional scenarios. A new adaptive adjustment to the R-2 statistic is proposed in this article, which applies to a general population model that covers the family of elliptical distributions and an independent compon...
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作者：Zhang, Zhe; Yu, Xiufan; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Notre Dame
摘要：This article proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully-designed projection matrix, the projection procedure enables us to transform the problem of inter...
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作者：Guo, Zijian; Li, Xiudi; Han, Larry; Cai, Tianxi
作者单位：Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Berkeley; Northeastern University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School
摘要：Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints. In this article, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the sites. Statistical inference for the prev...
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作者：Ignatiadis, Nikolaos; Sun, Dennis L.
作者单位：University of Chicago; Stanford University
