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作者：Kowal, Daniel R.; Wu, Bohan
作者单位：Cornell University; Rice University; Columbia University
摘要：Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This article introduces a simple, general, and efficient strategy for joint posterior...
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作者：Saha, Arkajyoti; Witten, Daniela; Bien, Jacob
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Southern California
摘要：We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables for which the correlation with all other variables falls below some threshold. Unlike other settings in selective inference, failure to account for the selection step leads to excessively conservative (as opposed to anti-conservative) results. We propose a new test that conditions on the event that t...
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作者：Cui, Yifan; Han, Sukjin
作者单位：Zhejiang University; Zhejiang University; University of Bristol
摘要：In this article, we explore optimal treatment allocation policies that target distributional welfare. Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE). While average welfare is intuitive, it may yield undesirable allocations especially when individuals are heterogeneous (e.g., with outliers)-the very reason individualized treatments were introduced in the first place. This observation motivates us to propose an optim...
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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; 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...
