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作者：Saevje, F.
作者单位：Yale University
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作者：Gorgi, P.; Lauria, C. S. A.; Luati, A.
作者单位：Vrije Universiteit Amsterdam; University of Bologna; Imperial College London
摘要：Score-driven models have recently been introduced as a general framework to specify time-varying parameters of conditional densities. The score enjoys stochastic properties that make these models easy to implement and convenient to apply in several contexts, ranging from biostatistics to finance. Score-driven parameter updates have been shown to be optimal in terms of locally reducing a local version of the Kullback-Leibler divergence between the true conditional density and the postulated den...
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作者：Chen, Rui; Huling, Jared D.; Chen, Guanhua; Yu, Menggang
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
摘要：Learning individualized treatment rules is an important topic in precision medicine. Current literature mainly focuses on deriving individualized treatment rules from a single source population. We consider the observational data setting when the source population differs from a target population of interest. Compared with causal generalization for the average treatment effect that is a scalar quantity, individualized treatment rule generalization poses new challenges due to the need to model ...
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作者：Alquier, P.; Gerber, M.
作者单位：ESSEC Business School; University of Bristol
摘要：Many modern datasets are collected automatically and are thus easily contaminated by outliers. This has led to a renewed interest in robust estimation, including new notions of robustness such as robustness to adversarial contamination of the data. However, most robust estimation methods are designed for a specific model. Notably, many methods were proposed recently to obtain robust estimators in linear models, or generalized linear models, and a few were developed for very specific settings, ...
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作者：Zhang, Zifeng; Ding, Peng; Zhou, Wen; Wang, Haonan
作者单位：Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Berkeley; New York University
摘要：Linear regression is arguably the most widely used statistical method. With fixed regressors and correlated errors, the conventional wisdom is to modify the variance-covariance estimator to accommodate the known correlation structure of the errors. We depart from existing literature by showing that with random regressors, linear regression inference is robust to correlated errors with unknown correlation structure. The existing theoretical analyses for linear regression are no longer valid bec...
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作者：Toulis, P.; Volfovsky, A.; Airoldi, E. M.
作者单位：University of Chicago; Duke University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：In many observational studies, the treatment assignment mechanism is not individualistic, as it allows the probability of treatment of a unit to depend on quantities beyond the unit's covariates. In such settings, unit treatments may be entangled in complex ways. In this article, we consider a particular instance of this problem where the treatments are entangled by a social network among units. For instance, when studying the effects of peer interaction on a social media platform, the treatme...
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作者：Zhao, Anqi; Ding, Peng; Li, Fan
作者单位：Duke University; University of California System; University of California Berkeley; Duke University
摘要：Covariate adjustment can improve precision in analysing randomized experiments. With fully observed data, regression adjustment and propensity score weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks down. Regression adjustment no lon...
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作者：Zhang, Jeffrey; Small, Dylan S.; Heng, Siyu
作者单位：University of Pennsylvania; New York University
摘要：Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion's sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well established for various matching designs and are commonly used in various studies. However, unlike the binary...
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作者：Gao, Zijun
作者单位：University of Southern California
摘要：The false discovery rate is a commonly used criterion in multiple testing, and the Benjamini-Hochberg procedure is a standard approach to false discovery rate control. To increase its power, adaptive Benjamini-Hochberg procedures, that use estimates of the null proportion, have been proposed. A particularly popular approach being that based on Storey's estimator. The performance of Storey's estimator hinges on a critical hyperparameter, such that a pre-fixed configuration may lack power and ex...
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作者：Li, Shuwei; Hu, Tao; Wang, Lianming; McMahan, Christopher S.; Tebbs, Joshua M.
作者单位：Guangzhou University; Capital Normal University; University of South Carolina System; University of South Carolina Columbia; Clemson University; University of South Carolina System; University of South Carolina Columbia
摘要：Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (). To ...
