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作者：Guo, F. Richard; Perkovic, Emilija; Rotnitzky, Andrea
作者单位：University of Cambridge; University of Washington; University of Washington Seattle; Universidad Torcuato Di Tella
摘要：We study efficient estimation of an interventional mean associated with a point exposure treatment under a causal graphical model represented by a directed acyclic graph without hidden variables. Under such a model, a subset of the variables may be uninformative, in that failure to measure them neither precludes identification of the interventional mean nor changes the semiparametric variance bound for regular estimators of it. We develop a set of graphical criteria that are sound and complete...
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作者：Ascolani, F.; Franzolini, B.; Lijoi, A.; Prunster, I
作者单位：Bocconi University
摘要：Modelling of the dependence structure across heterogeneous data is crucial for Bayesian inference, since it directly impacts the borrowing of information. Despite extensive advances over the past two decades, most available methods only allow for nonnegative correlations. We derive a new class of dependent nonparametric priors that can induce correlations of any sign, thus introducing a new and more flexible idea of borrowing of information. This is achieved thanks to a novel concept, which we...
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作者：Lou, Zhipeng; Zhang, Xianyang; Wu, Wei Biao
作者单位：Princeton University; Texas A&M University System; Texas A&M University College Station; University of Chicago
摘要：In this paper, we develop a systematic theory for high-dimensional analysis of variance in multivariate linear regression, where the dimension and the number of coefficients can both grow with the sample size. We propose a new U-type statistic to test linear hypotheses and establish a high-dimensional Gaussian approximation result under fairly mild moment assumptions. Our general framework and theory can be used to deal with the classical one-way multivariate analysis of variance, and the nonp...
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作者：Dimitriadis, Timo; Dumbgen, Lutz; Henzi, Alexander; Puke, Marius; Ziegel, Johanna
作者单位：Ruprecht Karls University Heidelberg; University of Bern; Swiss Federal Institutes of Technology Domain; ETH Zurich; University Hohenheim
摘要：Probability predictions from binary regressions or machine learning methods ought to be calibrated: if an event is predicted to occur with probability x, it should materialize with approximately that frequency, which means that the so-called calibration curvep(middot) should equal the identity, i.e., p(x) = x for all x in the unit interval. We propose honest calibration assessment based on novel confidence bands for the calibration curve, which are valid subject to only the natural assumption ...
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作者：Xu, Yangjianchen; Zeng, Donglin; Lin, D. Y.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseu...
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作者：Kuchibhotla, Arun Kumar; Balakrishnan, Sivaraman; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：We introduce a new notion of regularity of an estimator called median regularity. We prove that uniformly valid honest inference for a functional is possible if and only if there exists a median regular estimator of that functional. To the best of our knowledge, such a notion of regularity that is necessary for uniformly valid inference is unavailable in the literature.
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作者：Czado, C.; Van Keilegom, I
作者单位：Technical University of Munich; KU Leuven
摘要：Consider a survival time T that is subject to random right censoring, and suppose that T is stochastically dependent on the censoring time C. We are interested in the marginal distribution of T. This situation is often encountered in practice. Consider, for example, the case where T is a patient's time to death from a certain disease. Then the censoring time C could be the time until the patient leaves the study or the time until death from another cause. If the reason for leaving the study is...
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作者：Rasines, D. Garcia; Young, G. A.
作者单位：Imperial College London
摘要：We consider the problem of providing valid inference for a selected parameter in a sparse regression setting. It is well known that classical regression tools can be unreliable in this context because of the bias generated in the selection step. Many approaches have been proposed in recent years to ensure inferential validity. In this article we consider a simple alternative to data splitting based on randomizing the response vector, which allows for higher selection and inferential power than...
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作者：Wang, Minjie; Allen, Genevera, I
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Rice University
摘要：Structural learning of Gaussian graphical models in the presence of latent variables has long been a challenging problem. Chandrasekaran et al. (2012) proposed a convex program for estimating a sparse graph plus a low-rank term that adjusts for latent variables; however, this approach poses challenges from both computational and statistical perspectives. We propose an alternative, simple solution: apply a hard-thresholding operator to existing graph selection methods. Conceptually simple and c...
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作者：Rigon, Tommaso; Herring, Amy H.; Dunson, David B.
作者单位：University of Milano-Bicocca; Duke University
摘要：Loss-based clustering methods, such as k-means clustering and its variants, are standard tools for finding groups in data. However, the lack of quantification of uncertainty in the estimated clusters is a disadvantage. Model-based clustering based on mixture models provides an alternative approach, but such methods face computational problems and are highly sensitive to the choice of kernel. In this article we propose a generalized Bayes framework that bridges between these paradigms through t...
