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作者：Hojsgaard, S.; Lauritzen, S.
作者单位：Aalborg University; University of Copenhagen
摘要：In Gaussian graphical models, the likelihood equations must typically be solved iteratively. This paper investigates two algorithms: a version of iterative proportional scaling, which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance matrix by neighbourhood coordinate descent, which corresponds to the graphical lasso with zero penalty. For large, sparse graphs, the iterative proportional scaling algorithm appears feasible and has simpl...
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作者：Yang, Cheng-Han; Cheng, Yu-Jen
作者单位：University of Texas System; University of Texas Health Science Center Houston; National Tsing Hua University
摘要：We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with survival outcome variables, the...
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作者：Stensrud, M. J.; Laurendeau, J. D.; Sarvet, A. L.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal r...
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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...
