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作者：Das, Kiranmoy; Ghosh, Pulak; Daniels, Michael J.
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Indian Institute of Management (IIM System); Indian Institute of Management Bangalore; State University System of Florida; University of Florida
摘要：As the population of the older individuals continues to grow, it is important to study the relationship among the variables measuring financial health and physical health of the older individuals to better understand the demand for healthcare, and health insurance. We propose a semiparametric approach to jointly model these variables. We use data from the Health and Retirement Study which includes a set of correlated longitudinal variables measuring financial and physical health. In particular...
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作者：Awan, Jordan; Slavkovic, Aleksandra
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Differential privacy (DP) provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data. We address the problem of releasing a noisy real-valued statistic vectorT, a function of sensitive data under DP, via the class ofK-norm mechanisms with the goal of minimizing the noise added to achieve privacy. First, we introduce thesensitivity space of T, which extends the concepts of sensitivity polytope and sensi...
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作者：Sansom, Philip G.; Stephenson, David B.; Bracegirdle, Thomas J.
作者单位：University of Exeter; UK Research & Innovation (UKRI); Natural Environment Research Council (NERC); NERC British Antarctic Survey
摘要：Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationships shared by multiple climate models have the potential to constrain our uncertainty when combined with historical observations. We combine projections from 13 climate models with observational data to quantify the impact of emergent relationships...
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作者：Kivaranovic, Danijel; Leeb, Hannes
作者单位：University of Vienna
摘要：Valid inference after model selection is currently a very active area of research. The polyhedral method, introduced in an article by Lee et al., allows for valid inference after model selection if the model selection event can be described by polyhedral constraints. In that reference, the method is exemplified by constructing two valid confidence intervals when the Lasso estimator is used to select a model. We here study the length of these intervals. For one of these confidence intervals, wh...
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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：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：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：Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn
作者单位：University of Pennsylvania; Carnegie Mellon University
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作者：Liang, Muxuan; Zhao, Ying-Qi
作者单位：Fred Hutchinson Cancer Center
摘要：We discuss the results on improving the generalizability of individualized treatment rule following the work by Kallus and Mo et al. We note that the advocated weights in the work of Kallus are connected to the efficient score of the contrast function. We further propose a likelihood-ratio-based method (LR-ITR) to accommodate covariate shifts, and compare it to the CTE-DR-ITR method proposed by Mo et al. We provide the upper-bound on the risk function of the target population when both the cov...
