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作者：Telschow, Fabian J. E.; Cheng, Dan; Pranav, Pratyush; Schwartzman, Armin
作者单位：Humboldt University of Berlin; Arizona State University; Arizona State University-Tempe; Universite Claude Bernard Lyon 1; Ecole Normale Superieure de Lyon (ENS de LYON); University of California System; University of California San Diego
摘要：The expected Euler characteristic (EEC) of excursion sets of a smooth Gaussian-related random field over a compact manifold approximates the dis-tribution of its supremum for high thresholds. Viewed as a function of the excursion threshold, the EEC of a Gaussian-related field is expressed by the Gaussian kinematic formula (GKF) as a finite sum of known functions multi-plied by the Lipschitz-Killing curvatures (LKCs) of the generating Gaussian field. This paper proposes consistent estimators of...
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作者：Verzelen, Nicolas; Fromont, Magalie; Lerasle, Matthieu; Reynaud-Bouret, Patricia
作者单位：INRAE; Universite de Rennes; Institut Polytechnique de Paris; ENSAE Paris; Universite Cote d'Azur
摘要：Given a times series Y in Rn, with a piecewise constant mean and independent components, the twin problems of change-point detection and change-point localization, respectively amount to detecting the existence of times where the mean varies and estimating the positions of those changepoints. In this work, we tightly characterize optimal rates for both problems and uncover the phase transition phenomenon from a global testing problem to a local estimation problem. Introducing a suitable defini...
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作者：Celentano, Michael; Fan, Zhou; Mei, Song
作者单位：University of California System; University of California Berkeley; Yale University
摘要：We study mean-field variational Bayesian inference using the TAP approach, for Z2-synchronization as a prototypical example of a high -dimensional Bayesian model. We show that for any signal strength & lambda; > 1 (the weak-recovery threshold), there exists a unique local minimizer of the TAP free energy functional near the mean of the Bayes posterior law. Furthermore, the TAP free energy in a local neighborhood of this minimizer is strongly con-vex. Consequently, a natural-gradient/mirror-des...
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作者：Richardson, Thomas S.; Evans, Robin J.; Robins, James M.; Shpitser, Ilya
作者单位：University of Washington; University of Washington Seattle; University of Oxford; Harvard University; Johns Hopkins University
摘要：Conditional independence models associated with directed acyclic graphs (DAGs) may be characterized in at least three different ways: via a factorization, the global Markov property (given by the d-separation crite-rion), and the local Markov property. Marginals of DAG models also imply equality constraints that are not conditional independences; the well-known ???Verma constraint??? is an example. Constraints of this type are used for testing edges, and in a computationally efficient marginal...
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作者：Lopuhaa, Hendrik Paul; Gares, Valerie; Ruiz-Gazen, Anne
作者单位：Delft University of Technology; Universite de Rennes; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
摘要：We provide a unified approach to S-estimation in balanced linear models with structured covariance matrices. Of main interest are S-estimators for linear mixed effects models, but our approach also includes S-estimators in several other standard multivariate models, such as multiple regression, multivariate regression and multivariate location and scatter. We provide sufficient conditions for the existence of S-functionals and S-estimators, establish asymptotic properties such as consistency a...
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作者：Finke, Axel; Thiery, Alexandre H.
作者单位：Loughborough University; National University of Singapore
摘要：The iterated conditional sequential Monte Carlo (i-CSMC) algorithm from Andrieu, Doucet and Holenstein (J. R. Stat. Soc. Ser. B Stat. Methodol. 72 (2010) 269-342) is an MCMC approach for efficiently sampling from the joint posterior distribution of the T latent states in challenging time-series models, for example, in nonlinear or non-Gaussian state-space models. It is also the main ingredient in particle Gibbs samplers which infer unknown model parameters alongside the latent states. In this ...
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作者：Liu, Yang; Hu, Feifang
作者单位：Renmin University of China; George Washington University
摘要：Covariate-adaptive randomization (CAR) is commonly implemented in clinical trials to balance observed covariates. Recent studies have demonstrated the advantages of CAR procedures in balancing covariates and improving the subsequent statistical analysis. Covariate balance is crucial, but it is not a panacea for the valid statistical inferences. If the response to a treatment interacts with some unobserved covariates, the conclusion drawn from a CAR experiment may be affected, and thus, be inco...
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作者：Konen, Dimitri; Paindaveine, Davy
作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles
摘要：We propose a concept of quantiles for probability measures on the unit hypersphere Sd-1 of Rd. The innermost quantile is the Frechet median, that is, the L1-analog of the Frechet mean. The proposed quantiles mu au are di-rectional in nature: they are indexed by a scalar order alpha e [0, 1] and a unit vector u in the tangent space TmSd-1 to Sd-1 at m. To ensure computability in any dimension d, our quantiles are essentially obtained by considering the Euclidean (Chaudhuri (J. Amer. Statist. As...
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作者：Zhou, Quan; Chang, Hyunwoong
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：Structure learning via MCMC sampling is known to be very challenging because of the enormous search space and the existence of Markov equivalent DAGs. Theoretical results on the mixing behavior are lacking. In this work, we prove the rapid mixing of a random walk Metropolis-Hastings algorithm, which reveals that the complexity of Bayesian learning of sparse equivalence classes grows only polynomially in n and p, under some high-dimensional assumptions. A series of high-dimensional consistency ...
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作者：Belloni, Alexandre; Chen, Mingli; Padilla, Oscar Hernan Madrid; Wang, Zixuan (kevin)
作者单位：Duke University; University of Warwick; University of California System; University of California Los Angeles; Harvard University
摘要：We propose a generalization of the linear panel quantile regression model to accommodate both sparse and dense parts: sparse means that while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings. Such a structure poses problems for traditional sparse estimators, su...
