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作者：Blanchet, Jose; Murthy, Karthyek; Si, Nian
作者单位：Stanford University; Singapore University of Technology & Design
摘要：Estimators based on Wasserstein distributionally robust optimization are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance from the underlying empirical measure in a Wasserstein sense. While motivated by the need to identify optimal model parameters or decision choices that are robust to model misspecification, these distributionally robust estimators recover a wide range...
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作者：Henzi, Alexander; Ziegel, Johanna F.
作者单位：University of Bern
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作者：Xia, Fan; Chan, Kwun Chuen Gary
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：Natural mediation effects are desirable estimands for studying causal mechanisms in a population, but complications arise in defining and estimating natural indirect effects through multiple mediators with an unspecified causal ordering. We propose a decomposition of the natural indirect effect of multiple mediators into individual components, termed exit indirect effects, and a remainder interaction term, and study the similarities to and differences from existing natural and interventional e...
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作者：Demirkaya, Emre; Feng, Yang; Basu, Pallavi; Lv, Jinchi
作者单位：University of Tennessee System; University of Tennessee Knoxville; New York University; Indian School of Business (ISB); University of Southern California
摘要：Model selection is crucial both to high-dimensional learning and to inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work implicitly assumes that the models are correctly specified or have fixed dimensionality, yet both model misspecification and high dimensionality are prevalent in practice. In this paper, we exploit the framework of model selection principles under the misspecified gen...
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作者：Ye, Ting; Yi, Yanyao; Shao, Jun
作者单位：University of Pennsylvania; Eli Lilly; East China Normal University
摘要：Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive randomization is mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate levels utilized in randomizat...
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作者：Gorsky, S.; Ma, L.
作者单位：University of Massachusetts System; University of Massachusetts Amherst; Duke University
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作者：Gorsky, S.; Ma, L.
作者单位：University of Massachusetts System; University of Massachusetts Amherst; Duke University
摘要：Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational ...
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作者：Escobar-Bach, Mikael; Maller, Ross; Van Keilegom, Ingrid; Zhao, Muzhi
作者单位：Universite d'Angers; Australian National University; KU Leuven
摘要：Estimators of the cured proportion from survival data which may include observations on cured subjects can only be expected to perform well when the follow-up period is sufficient. When follow-up is not sufficient, and the survival distribution of those susceptible to the event belongs to the Frechet maximum domain of attraction, a nonparametric estimator for the cure proportion proposed by incorporates an adjustment that reduces the bias in the usual estimator. Besides the Frechet, an importa...
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作者：Park, Juhyun; Ahn, Jeongyoun; Jeon, Yongho
作者单位：Universite Paris Saclay; Universite Paris Saclay; University System of Georgia; University of Georgia; Yonsei University
摘要：Functional linear discriminant analysis provides a simple yet efficient method for classification, with the possibility of achieving perfect classification. Several methods have been proposed in the literature that mostly address the dimensionality of the problem. On the other hand, there is growing interest in interpretability of the analysis, which favours a simple and sparse solution. In this paper we propose a new approach that incorporates a type of sparsity that identifies nonzero subdom...
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作者：Zhang, Xuefei; Xu, Gongjun; Zhu, Ji
作者单位：University of Michigan System; University of Michigan
摘要：Network latent space models assume that each node is associated with an unobserved latent position in a Euclidean space, and such latent variables determine the probability of two nodes connecting with each other. In many applications, nodes in the network are often observed along with high-dimensional node variables, and these node variables provide important information for understanding the network structure. However, classical network latent space models have several limitations in incorpo...
