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作者：Vanderweele, Tyler J.; Tan, Zhiqiang
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Rutgers University System; Rutgers University New Brunswick
摘要：We give a definition of a bounded edge within the causal directed acyclic graph framework. A bounded edge generalizes the notion of a signed edge and is defined in terms of bounds on a ratio of survivor probabilities. We derive rules concerning the propagation of bounds. Bounds on causal effects in the presence of unmeasured confounding are also derived using bounds related to specific edges on a graph. We illustrate the theory developed by an example concerning estimating the effect of antihi...
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作者：Kim, Yongdai; Kwon, Sunghoon
作者单位：Seoul National University (SNU); University of Minnesota System; University of Minnesota Twin Cities
摘要：Nonconvex penalties such as the smoothly clipped absolute deviation or minimax concave penalties have desirable properties such as the oracle property, even when the dimension of the predictive variables is large. However, checking whether a given local minimizer has such properties is not easy since there can be many local minimizers. In this paper, we give sufficient conditions under which a local minimizer is unique, and show that the oracle estimator becomes the unique local minimizer with...
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作者：Jiang, Binyan; Loh, Wei-Liem
作者单位：National University of Singapore
摘要：This article proposes a method of moments technique for estimating the sparsity of signals in a random sample. This involves estimating the largest eigenvalue of a large Hermitian trigonometric matrix under mild conditions. As illustration, the method is applied to two well-known problems. The first focuses on the sparsity of a large covariance matrix and the second investigates the sparsity of a sequence of signals observed with stationary, weakly dependent noise. Simulation shows that the pr...
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作者：Rotnitzky, Andrea; Lei, Quanhong; Sued, Mariela; Robins, James M.
作者单位：Universidad Torcuato Di Tella; University of Buenos Aires; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency ...
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作者：Rosenbaum, P. R.
作者单位：University of Pennsylvania
摘要：In a matched observational study of treatment effects, a sensitivity analysis asks about the magnitude of the departure from random assignment that would need to be present to alter the conclusions of an analysis that assumes that matching for measured covariates removes all bias. The reported degree of sensitivity to unmeasured biases depends on both the process that generated the data and the chosen methods of analysis, so a poor choice of method may lead to an exaggerated report of sensitiv...
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作者：Chen, Li; Lin, D. Y.; Zeng, Donglin
作者单位：University of Kentucky; University of Kentucky; University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a graphical measure, the generalized negative predictive function, to quantify the predictive accuracy of covariates for survival time or recurrent event times. This new measure characterizes the event-free probabilities over time conditional on a thresholded linear combination of covariates and has direct clinical utility. We show that this function is maximized at the set of covariates truly related to event times and thus can be used to compare the predictive accuracy of differen...
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作者：Jung, Sungkyu; Dryden, Ian L.; Marron, J. S.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of South Carolina System; University of South Carolina Columbia; University of North Carolina; University of North Carolina Chapel Hill
摘要：A general framework for a novel non-geodesic decomposition of high-dimensional spheres or high-dimensional shape spaces for planar landmarks is discussed. The decomposition, principal nested spheres, leads to a sequence of submanifolds with decreasing intrinsic dimensions, which can be interpreted as an analogue of principal component analysis. In a number of real datasets, an apparent one-dimensional mode of variation curving through more than one geodesic component is captured in the one-dim...
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作者：Delaigle, A.; Hall, P.; Bathia, N.
作者单位：University of Melbourne
摘要：The infinite dimension of functional data can challenge conventional methods for classification and clustering. A variety of techniques have been introduced to address this problem, particularly in the case of prediction, but the structural models that they involve can be too inaccurate, or too abstract, or too difficult to interpret, for practitioners. In this paper, we develop approaches to adaptively choose components, enabling classification and clustering to be reduced to finite-dimension...
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作者：Wadsworth, Jennifer L.; Tawn, Jonathan A.
作者单位：Lancaster University
摘要：Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model dependence for spatial extremes: it must be assumed that the dependence structure of the observed extremes is compatible with a limiting model that holds for all events more extreme than those that ha...
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作者：Wang, H.
作者单位：Peking University
摘要：We propose a method of factor profiled sure independence screening for ultrahigh-dimensional variable selection. The objective of this method is to identify nonzero components consistently from a sparse coefficient vector. The new method assumes that the correlation structure of the high-dimensional data can be well represented by a set of low-dimensional latent factors, which can be estimated consistently by eigenvalue-eigenvector decomposition. The estimated latent factors should then be pro...
