3期

最新文章

• THE ZIG-ZAG PROCESS AND SUPER-EFFICIENT SAMPLING FOR BAYESIAN ANALYSIS OF BIG DATA AI翻译
  作者：Ierkens, Joris B.; Fearnhead, Paul; Roberts, Gareth
  作者单位：Delft University of Technology; Delft University of Technology; Lancaster University; University of Warwick
  摘要：Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration. There have been a number of approximate MCMC algorithms that use sub-sampling ideas to reduce this computational burden, but with the drawback that these algorithms no longer target the true posterior distribution. We introduce a new family of Monte Carlo methods based upon a multidimensional version of the Zig-Zag process of [Ann. Appl. Probab. 27 (2017) 846-882], a contin...
• EXPONENTIAL ERGODICITY OF THE BOUNCY PARTICLE SAMPLER AI翻译
  作者：Deligiannidis, George; Bouchard-Cote, Alexandre; Doucet, Arnaud
  作者单位：University of Oxford; University of British Columbia
  摘要：Nonreversible Markov chain Monte Carlo schemes based on piecewise deterministic Markov processes have been recently introduced in applied probability, automatic control, physics and statistics. Although these algorithms demonstrate experimentally good performance and are accordingly increasingly used in a wide range of applications, geometric ergodicity results for such schemes have only been established so far under very restrictive assumptions. We give here verifiable conditions on the targe...
• THE MAXIMUM LIKELIHOOD THRESHOLD OF A PATH DIAGRAM AI翻译
  作者：Drton, Mathias; Fox, Christopher; Kaeufl, Andreas; Pouliot, Guillaume
  作者单位：University of Washington; University of Washington Seattle; University of Copenhagen; University of Chicago; University of Augsburg; University of Chicago; Universite de Montreal; Polytechnique Montreal
  摘要：Linear structural equation models postulate noisy linear relationships between variables of interest. Each model corresponds to a path diagram, which is a mixed graph with directed edges that encode the domains of the linear functions and bidirected edges that indicate possible correlations among noise terms. Using this graphical representation, we determine the maximum likelihood threshold, that is, the minimum sample size at which the likelihood function of a Gaussian structural equation mod...
• BOOTSTRAP TUNING IN GAUSSIAN ORDERED MODEL SELECTION AI翻译
  作者：Spokoiny, Vladimir; Willrich, Niklas
  作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics; Humboldt University of Berlin; Russian Academy of Sciences; HSE University (National Research University Higher School of Economics)
  摘要：The paper focuses on the problem of model selection in linear Gaussian regression with unknown possibly inhomogeneous noise. For a given family of linear estimators {(theta) over tilde (m), m is an element of M}, ordered by their variance, we offer a new smallest accepted approach motivated by Lepski's device and the multiple testing idea. The procedure selects the smallest model which satisfies the acceptance rule based on comparison with all larger models. The method is completely data-drive...
• CHIME: CLUSTERING OF HIGH-DIMENSIONAL GAUSSIAN MIXTURES WITH EM ALGORITHM AND ITS OPTIMALITY AI翻译
  作者：Cai, T. Tony; Ma, Jing; Zhang, Linjun
  作者单位：University of Pennsylvania
  摘要：Unsupervised learning is an important problem in statistics and machine learning with a wide range of applications. In this paper, we study clustering of high-dimensional Gaussian mixtures and propose a procedure, called CHIME, that is based on the EM algorithm and a direct estimation method for the sparse discriminant vector. Both theoretical and numerical properties of CHIME are investigated. We establish the optimal rate of convergence for the excess misclustering error and show that CHIME ...
• A ROBUST AND EFFICIENT APPROACH TO CAUSAL INFERENCE BASED ON SPARSE SUFFICIENT DIMENSION REDUCTION AI翻译
  作者：Ma, Shujie; Zhu, Liping; Zhang, Zhiwei; Tsai, Chih-Ling; Carroll, Raymond J.
  作者单位：University of California System; University of California Riverside; Renmin University of China; University of California System; University of California Davis; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most exi...
• GAUSSIAN APPROXIMATION OF MAXIMA OF WIENER FUNCTIONALS AND ITS APPLICATION TO HIGH-FREQUENCY DATA AI翻译
  作者：Koike, Yuta
  作者单位：University of Tokyo; Japan Science & Technology Agency (JST)
  摘要：This paper establishes an upper bound for the Kolmogorov distance between the maximum of a high-dimensional vector of smooth Wiener functionals and the maximum of a Gaussian random vector. As a special case, we show that the maximum of multiple Wiener-Ito integrals with common orders is well approximated by its Gaussian analog in terms of the Kolmogorov distance if their covariance matrices are close to each other and the maximum of the fourth cumulants of the multiple Wiener-Ito integrals is ...
• PREDICTION WHEN FITTING SIMPLE MODELS TO HIGH-DIMENSIONAL DATA AI翻译
  作者：Steinberger, Lukas; Leeb, Hannes
  作者单位：University of Freiburg; University of Vienna
  摘要：We study linear subset regression in the context of a high-dimensional linear model. Consider y = v + theta' z + epsilon with univariate response y and a d-vector of random regressors z, and a submodel where y is regressed on a set of p explanatory variables that are given by x = M' z, for some d x p matrix M. Here, high-dimensional means that the number d of available explanatory variables in the overall model is much larger than the number p of variables in the submodel. In this paper, we pr...
• CONVEX REGULARIZATION FOR HIGH-DIMENSIONAL MULTIRESPONSE TENSOR REGRESSION AI翻译
  作者：Raskutti, Garvesh; Yuan, Ming; Chen, Han
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Columbia University
  摘要：In this paper, we present a general convex optimization approach for solving high-dimensional multiple response tensor regression problems under low-dimensional structural assumptions. We consider using convex and weakly decomposable regularizers assuming that the underlying tensor lies in an unknown low-dimensional subspace. Within our framework, we derive general risk bounds of the resulting estimate under fairly general dependence structure among covariates. Our framework leads to upper bou...
• CAUSAL DANTZIG: FAST INFERENCE IN LINEAR STRUCTURAL EQUATION MODELS WITH HIDDEN VARIABLES UNDER ADDITIVE INTERVENTIONS AI翻译
  作者：Rothenhausler, Dominik; Buhlmann, Peter; Meinshausen, Nicolai
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Causal inference is known to be very challenging when only observational data are available. Randomized experiments are often costly and impractical and in instrumental variable regression the number of instruments has to exceed the number of causal predictors. It was recently shown in Peters, Buhlmann and Meinshausen (2016) (J. R. Stat. Soc. Ser. B. Stat. Methodol. 78 947-1012) that causal inference for the full model is possible when data from distinct observational environments are availabl...