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作者：Hector, Emily C.; Song, Peter X-K
作者单位：University of Michigan System; University of Michigan
摘要：This article is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multilevel nested correlations. We develop a divide-and-conquer procedure implemented in a fully distributed and parallelized computational scheme for statistical estimation and inference of regression parameters. Despite significant efforts in the literature, the computational bottleneck associated with high-dimensional likelihoods prevents the s...
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作者：Hao, Meiling; Liu, Kin-yat; Xu, Wei; Zhao, Xingqiu
作者单位：University of International Business & Economics; Hang Seng University of Hong Kong; University of Toronto; Hong Kong Polytechnic University
摘要：This article studies penalized semiparametric maximum partial likelihood estimation and hypothesis testing for the functional Cox model in analyzing right-censored data with both functional and scalar predictors. Deriving the asymptotic joint distribution of finite-dimensional and infinite-dimensional estimators is a very challenging theoretical problem due to the complexity of semiparametric models. For the problem, we construct the Sobolev space equipped with a special inner product and disc...
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作者：Rudolph, Kara E.; Sofrygin, Oleg; van der Laan, Mark J.
作者单位：Columbia University; University of California System; University of California Berkeley
摘要：Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this article, we identify the instrumental variable-direct effect of the exposure on the outcome not through the mediator, using randomization of the instrument. We call this estimand the complier stochastic direct effect (CSDE). To our knowledge, such an estimand has not previously been considered or estimated. We propose and evaluate several estimators for the CSDE: a ratio of inverse...
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作者：Agarwal, Anish; Shah, Devavrat; Shen, Dennis; Song, Dogyoon
作者单位：Massachusetts Institute of Technology (MIT)
摘要：Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued, that is, discrete and continuous, covariates is not understood and remains an important open challenge. As the main contribution of this work, we establish the robustness of PCR, without any change, in this respect and provide meaningfu...
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作者：Mo, Weibin; Qi, Zhengling; Liu, Yufeng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; George Washington University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and testing distributions are the same. However, the estimated optimal ITR may have poor generalizability when the training and testing distr...
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作者：Lunagomez, Simon; Olhede, Sofia C.; Wolfe, Patrick J.
作者单位：Lancaster University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of London; University College London; Purdue University System; Purdue University; Purdue University System; Purdue University; Purdue University System; Purdue University
摘要：This article introduces a new class of models for multiple networks. The core idea is to parameterize a distribution on labeled graphs in terms of a Frechet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter that controls the concentration of this distribution about its mean. Entropy is the natural parameter for such control, varying from a point mass concentrated on the Frechet mean itself to a uniform distribution over all graphs on a given vert...
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作者：Sekhon, Jasjeet S.; Shem-Tov, Yotam
作者单位：Yale University; Yale University; University of California System; University of California Los Angeles
摘要：We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show that when the researcher's objective is inference on sample average treatment effect of the treated (SATT), a consistent variance estimator exists. Although this estimand is equal to the sample average treatment effect (SATE) in expectation, potentially large differences in both accuracy and coverage c...
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作者：Liu, Dungang; Li, Shaobo; Yu, Yan; Moustaki, Irini
作者单位：University System of Ohio; University of Cincinnati; University of Kansas; University of London; London School Economics & Political Science
摘要：Partial association refers to the relationship between variableswhile adjusting for a set of covariates. To assess such an association whenY(k)'s are recorded on ordinal scales, a classical approach is to use partial correlation between the latent continuous variables. This so-called polychoric correlation is inadequate, as it requires multivariate normality and it only reflects a linear association. We propose a new framework for studying ordinal-ordinal partial association by using Liu-Zhang...
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作者：Hu, Xinyu; Qian, Min; Cheng, Bin; Cheung, Ying Kuen
作者单位：Columbia University
摘要：Personalized policy represents a paradigm shift one decision rule for all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population features and individual f...
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作者：Xue, Fei; Qu, Annie
作者单位：University of Pennsylvania; University of California System; University of California Irvine
摘要：For multisource data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this article, we propose a multiple block-wise imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed va...
