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作者：Chang, Ming-Chung; Cheng, Ching-Shui
作者单位：Academia Sinica - Taiwan; University of California System; University of California Berkeley
摘要：In a multi-stratum factorial experiment, there are multiple error terms (strata) with different variances that arise from complicated structures of the experimental units. For unstructured experimental units, minimum aberration is a popular criterion for choosing regular fractional factorial designs. One difficulty in extending this criterion to multi-stratum factorial designs is that the formulation of a word length pattern based on which minimum aberration is defined requires an order of des...
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作者：Brown, Lawrence D.; Mukherjee, Gourab; Weinstein, Asaf
作者单位：University of Pennsylvania; University of Southern California; Stanford University
摘要：We develop an empirical Bayes procedure for estimating the cell means in an unbalanced, two-way additive model with fixed effects. We employ a hierarchical model, which reflects exchangeability of the effects within treatment and within block but not necessarily between them, as suggested before by Lindley and Smith [J. R. Stat. Soc., B 34 (1972) 1-41]. The hyperparameters of this hierarchical model, instead of considered fixed, are to be substituted with data-dependent values in such a way th...
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作者：Juditsky, Anatoli; Nemirovski, Arkadi
作者单位：Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); University System of Georgia; Georgia Institute of Technology
摘要：We consider the problem of recovering linear image Bx of a signal x known to belong to a given convex compact set chi from indirect observation omega = Ax + sigma xi of x corrupted by Gaussian noise xi. It is shown that under some assumptions on chi (satisfied, e.g., when chi is the intersection of K concentric ellipsoids/elliptic cylinders), an easy-to-compute linear estimate is near-optimal in terms of its worst case, over x is an element of chi, expected parallel to.parallel to(2)(2)-loss. ...
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作者：Chakrabortty, Abhishek; Cai, Tianxi
作者单位：University of Pennsylvania; Harvard University
摘要：We consider the linear regression problem under semi-supervised settings wherein the available data typically consists of: (i) a small or moderate sized labeled data, and (ii) a much larger sized unlabeled data. Such data arises naturally from settings where the outcome, unlike the covariates, is expensive to obtain, a frequent scenario in modern studies involving large databases like electronic medical records (EMR). Supervised estimators like the ordinary least squares (OLS) estimator utiliz...
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作者：Chen, Yudong; Li, Xiaodong; Xu, Jiaming
作者单位：Cornell University; University of California System; University of California Davis; Purdue University System; Purdue University
摘要：The stochastic block model (SBM), a popular framework for studying community detection in networks, is limited by the assumption that all nodes in the same community are statistically equivalent and have equal expected degrees. The degree-corrected stochastic block model (DCSBM) is a natural extension of SBM that allows for degree heterogeneity within communities. To find the communities under DCSBM, this paper proposes a convexified modularity maximization approach, which is based on a convex...
