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作者：Liu, Han; Han, Fang; Yuan, Ming; Lafferty, John; Wasserman, Larry
作者单位：Princeton University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University System of Georgia; Georgia Institute of Technology; University of Chicago; Carnegie Mellon University
摘要：We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly estimating high-dimensional undirected graphical models. To achieve modeling flexibility, we consider the nonparanormal graphical models proposed by Liu, Lafferty and Wasserman [J. Mach. Learn. Res. 10 (2009) 2295-2328]. To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman's rho and Kendall's tau. We prove that the nonparanor...
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作者：Soltanolkotabi, Mahdi; Candes, Emmanuel J.
作者单位：Stanford University
摘要：This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower-dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named sparse subspace clustering (SSC) [In IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 (2...
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作者：Lauritzen, Steffen; Meinshausen, Nicolai
作者单位：University of Oxford
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作者：Fellouris, Georgios
作者单位：University of Southern California
摘要：A parameter estimation problem is considered, in which dispersed sensors transmit to the statistician partial information regarding their observations. The sensors observe the paths of continuous semimartingales, whose drifts are linear with respect to a common parameter. A novel estimating scheme is suggested, according to which each sensor transmits only one-bit messages at stopping times of its local filtration. The proposed estimator is shown to be consistent and, for a large class of proc...
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作者：Wainwright, Martin J.
作者单位：University of California System; University of California Berkeley
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作者：Fan, Yingying; Li, Runze
作者单位：University of Southern California; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：This paper is concerned with the selection and estimation of fixed and random effects in linear mixed effects models. We propose a class of nonconcave penalized profile likelihood methods for selecting and estimating important fixed effects. To overcome the difficulty of unknown covariance matrix of random effects, we propose to use a proxy matrix in the penalized profile likelihood. We establish conditions on the choice of the proxy matrix and show that the proposed procedure enjoys the model...
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作者：VanderWeele, Tyler J.; Richardson, Thomas S.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle
摘要：The sufficient-component cause framework assumes the existence of sets of sufficient causes that bring about an event. For a binary outcome and an arbitrary number of binary causes any set of potential outcomes can be replicated by positing a set of sufficient causes; typically this representation is not unique. A sufficient cause interaction is said to be present if within all representations there exists a sufficient cause in which two or more particular causes are all present. A singular in...
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作者：Roysland, Kjetil
作者单位：University of Oslo
摘要：We show that one can perform causal inference in a natural way for continuous-time scenarios using tools from stochastic analysis. This provides new alternatives to the positivity condition for inverse probability weighting. The probability distribution that would govern the frequency of observations in the counterfactual scenario can be characterized in terms of a so-called martingale problem. The counterfactual and factual probability distributions may be related through a likelihood ratio g...
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作者：Dalalyan, Arnak S.; Salmon, Joseph
作者单位：Institut Polytechnique de Paris; ENSAE Paris; Universite Paris Cite
摘要：We consider the problem of combining a (possibly uncountably infinite) set of affine estimators in nonparametric regression model with heteroscedastic Gaussian noise. Focusing on the exponentially weighted aggregate, we prove a PAC-Bayesian type inequality that leads to sharp oracle inequalities in discrete but also in continuous settings. The framework is general enough to cover the combinations of various procedures such as least square regression, kernel ridge regression, shrinking estimato...
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作者：Cisewski, Jessi; Hannig, Jan
作者单位：Carnegie Mellon University; University of North Carolina; University of North Carolina Chapel Hill
摘要：While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced setting. Generalized fiducial inference provides a possible framework that accommodates this absence of methodology. Under the fabric of generalized fiducial inference along with sequential Monte Carlo methods, we present an approach for interval...
