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作者：Woody, S.; Padilla, O. H. M.; Scott, J. G.
作者单位：University of Texas System; University of Texas Austin; University of California System; University of California Los Angeles; University of Texas System; University of Texas Austin
摘要：Many recently developed Bayesian methods focus on sparse signal detection. However, much less work has been done on the natural follow-up question: how does one make valid inferences for the magnitude of those signals after selection? Ordinary Bayesian credible intervals suffer from selection bias, as do ordinary frequentist confidence intervals. Existing Bayesian methods for correcting this bias produce credible intervals with poor frequentist properties. Further, existing frequentist approac...
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作者：Zhao, Junlong; Liu, Xiumin; Wang, Hansheng; Leng, Chenlei
作者单位：Beijing Normal University; Peking University; University of Warwick
摘要：A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension pro...
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作者：Park, C.; Kang, H.
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Although many estimators for network treatment effects have been proposed, their optimality properties, in terms of semiparametric efficiency, have yet to be resolved. We present a simple yet flexible asymptotic framework for deriving the efficient influence function and the semiparametric efficiency lower bound for a family of network causal effects under partial interference. An important corollary of our results is that one existing estimator, that proposed by , is locally efficient. We als...
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作者：Lee, D.; El-Zaatari, H.; Kosorok, M. R.; Li, X.; Zhang, K.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Clemson University; University of North Carolina; University of North Carolina Chapel Hill
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作者：Kong, Dehan
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作者：Ciocanea-Teodorescu, I; Gabriel, E. E.; Sjolander, A.
作者单位：Karolinska Institutet; University of Copenhagen
摘要：One of the main threats to the validity of causal effect estimates from observational data is the existence of unmeasured confounders. A plethora of methods has been proposed to quantify deviation from conditional exchangeability, which arises when confounding is not properly accounted for, with each method having its own set of limitations and underlying assumptions. Few methods both scale well with the increasing complexity of potential measured confounders and avoid making strong simplifyin...
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作者：Xu, Jason; Lange, Kenneth
作者单位：Duke University; University of California System; University of California Los Angeles
摘要：This paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrai...
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作者：Mao, Lu
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：A general framework is set up to study the asymptotic properties of the intent-to-treat Wilcoxon-Mann-Whitney test in randomized experiments with nonignorable noncompliance. Under location-shift alternatives, the Pitman efficiencies of the intent-to-treat Wilcoxon-Mann-Whitney and t tests are derived. It is shown that the former is superior if the compliers are more likely to be found in high-density regions of the outcome distribution or, equivalently, if the noncompliers tend to reside in th...
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作者：Ying, Chao; Yu, Zhou
作者单位：East China Normal University
摘要：We consider Frechet sufficient dimension reduction with responses being complex random objects in a metric space and high-dimensional Euclidean predictors. We propose a novel approach, called the weighted inverse regression ensemble method, for linear Frechet sufficient dimension reduction. The method is further generalized as a new operator defined on reproducing kernel Hilbert spaces for nonlinear Frechet sufficient dimension reduction. We provide theoretical guarantees for the new method vi...
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作者：Mammen, E.; Sperlich, S.
作者单位：Ruprecht Karls University Heidelberg; University of Geneva
摘要：We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All estimators are obtained by smoo...
