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作者：Gronsbell, Jessica L.; Cai, Tianxi
作者单位：Harvard University
摘要：In many modern machine learning applications, the outcome is expensive or time consuming to collect whereas the predictor information is easy to obtain. Semi-supervised (SS) learning aims at utilizing large amounts of unlabelled' data along with small amounts of labelled' data to improve the efficiency of a classical supervised approach. Though numerous SS learning classification and prediction procedures have been proposed in recent years, no methods currently exist to evaluate the prediction...
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作者：Yao, Shun; Zhang, Xianyang; Shao, Xiaofeng
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Texas A&M University System; Texas A&M University College Station
摘要：We introduce an L2-type test for testing mutual independence and banded dependence structure for high dimensional data. The test is constructed on the basis of the pairwise distance covariance and it accounts for the non-linear and non-monotone dependences among the data, which cannot be fully captured by the existing tests based on either Pearson correlation or rank correlation. Our test can be conveniently implemented in practice as the limiting null distribution of the test statistic is sho...
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作者：Lauritzen, Steffen; Rinaldo, Alessandro; Sadeghi, Kayvan
作者单位：University of Copenhagen; Carnegie Mellon University; University of Cambridge
摘要：We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical subgraph densities are maximum likelihood estimates of their theoretical counterparts. We then characterize all possible Markov structures for finitely exchangeable random graphs, thereby identifying a new class of Markov network models corresponding to bidirected Kneser graphs. In particular, we demonstrate that the...
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作者：Candes, Emmanuel; Fan, Yingying; Janson, Lucas; Lv, Jinchi
作者单位：Stanford University; University of Southern California
摘要：Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a non-linear fashion, such as when the response is binary. Although this modelling problem has been extensively studied, it remains unclear how to control the fraction of false discoveries effectively even in high dimensional logistic regression, not to mention general high dimensional non-linear models. To address such a practical problem, we propose a ...
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作者：Aue, Alexander; Rice, Gregory; Sonmez, Ozan
作者单位：University of California System; University of California Davis; University of Waterloo
摘要：Methodology is proposed to uncover structural breaks in functional data that is fully functional' in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight that the fully functional pr...
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作者：Wang, Linbo; Tchetgen, Eric Tchetgen
作者单位：Harvard University; Harvard T.H. Chan School of Public Health
摘要：Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard instrumental variable model, however, the average treatment effect is only partially identifiable. To address this, we propose novel assumptions that enable identification of the average treatment effect. Our identification assumptions are clearly separated from model assumptions that are needed for estimation, so researchers are not required to commit to a specifi...
