5期

最新文章

• A GENERAL FRAMEWORK FOR BAYES STRUCTURED LINEAR MODELS AI翻译
  作者：Gao, Chao; van der Vaart, Aad W.; Zhou, Harrison H.
  作者单位：University of Chicago; Leiden University; Leiden University - Excl LUMC; Yale University
  摘要：High dimensional statistics deals with the challenge of extracting structured information from complex model settings. Compared with a large number of frequentist methodologies, there are rather few theoretically optimal Bayes methods for high dimensional models. This paper provides a unified approach to both Bayes high dimensional statistics and Bayes nonparametrics in a general framework of structured linear models. With a proposed two-step prior, we prove a general oracle inequality for pos...
• COUPLED CONDITIONAL BACKWARD SAMPLING PARTICLE FILTER AI翻译
  作者：Lee, Anthony; Singh, Sumeetpal S.; Vihola, Matti
  作者单位：University of Bristol; University of Cambridge; University of Jyvaskyla
  摘要：The conditional particle filter (CPF) is a promising algorithm for general hidden Markov model smoothing. Empirical evidence suggests that the variant of CPF with backward sampling (CBPF) performs well even with long time series. Previous theoretical results have not been able to demonstrate the improvement brought by backward sampling, whereas we provide rates showing that CBPF can remain effective with a fixed number of particles independent of the time horizon. Our result is based on analys...
• A FRAMEWORK FOR ADAPTIVE MCMC TARGETING MULTIMODAL DISTRIBUTIONS AI翻译
  作者：Pompe, Emilia; Holmes, Chris; Latuszynski, Krzysztof
  作者单位：University of Oxford; University of Warwick
  摘要：We propose a new Monte Carlo method for sampling from multimodal distributions. The idea of this technique is based on splitting the task into two: finding the modes of a target distribution pi and sampling, given the knowledge of the locations of the modes. The sampling algorithm relies on steps of two types: local ones, preserving the mode; and jumps to regions associated with different modes. Besides, the method learns the optimal parameters of the algorithm, while it runs, without requirin...
• MINIMAX OPTIMAL SEQUENTIAL HYPOTHESIS TESTS FOR MARKOV PROCESSES AI翻译
  作者：Fauss, Michael; Zoubir, Abdelhak M.; Poor, H. Vincent
  作者单位：Technical University of Darmstadt; Princeton University
  摘要：Under mild Markov assumptions, sufficient conditions for strict minimax optimality of sequential tests for multiple hypotheses under distributional uncertainty are derived. First, the design of optimal sequential tests for simple hypotheses is revisited, and it is shown that the partial derivatives of the corresponding cost function are closely related to the performance metrics of the underlying sequential test. Second, an implicit characterization of the least favorable distributions for a g...
• RELAXING THE ASSUMPTIONS OF KNOCKOFFS BY CONDITIONING AI翻译
  作者：Huang, Dongming; Janson, Lucas
  作者单位：Harvard University
  摘要：The recent paper Candes et al. (J. R. Stat. Soc. Ser. B. Stat. Methodol. 80 (2018) 551-577) introduced model-X knockoffs, a method for variable selection that provably and nonasymptotically controls the false discovery rate with no restrictions or assumptions on the dimensionality of the data or the conditional distribution of the response given the covariates. The one requirement for the procedure is that the covariate samples are drawn independently and identically from a precisely-known (bu...
• TEST OF SIGNIFICANCE FOR HIGH-DIMENSIONAL LONGITUDINAL DATA AI翻译
  作者：Fang, Ethan X.; Ning, Yang; Li, Runze
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Cornell University
  摘要：This paper concerns statistical inference for longitudinal data with ultrahigh dimensional covariates. We first study the problem of constructing confidence intervals and hypothesis tests for a low-dimensional parameter of interest. The major challenge is how to construct a powerful test statistic in the presence of high-dimensional nuisance parameters and sophisticated within-subject correlation of longitudinal data. To deal with the challenge, we propose a new quadratic decorrelated inferenc...
• SEMIPARAMETRIC BAYESIAN CAUSAL INFERENCE AI翻译
  作者：Ray, Kolyan; van der Vaart, Aad
  作者单位：Imperial College London; Leiden University; Leiden University - Excl LUMC
  摘要：We develop a semiparametric Bayesian approach for estimating the mean response in a missing data model with binary outcomes and a nonparametrically modelled propensity score. Equivalently, we estimate the causal effect of a treatment, correcting nonparametrically for confounding. We show that standard Gaussian process priors satisfy a semiparametric Bernsteinvon Mises theorem under smoothness conditions. We further propose a novel propensity score-dependent prior that provides efficient infere...
• CONTROLLED SEQUENTIAL MONTE CARLO AI翻译
  作者：Heng, Jeremy; Bishop, Adrian N.; Deligiannidis, George; Doucet, Arnaud
  作者单位：ESSEC Business School; Commonwealth Scientific & Industrial Research Organisation (CSIRO); University of Oxford
  摘要：Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and related fields; for example, for inference in nonlinear non-Gaussian state space models, and in complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which crucially impact their performance. We int...
• ADDITIVE REGRESSION WITH HILBERTIAN RESPONSES AI翻译
  作者：Jeon, Jeong Min; Park, Byeong U.
  作者单位：Seoul National University (SNU)
  摘要：This paper develops a foundation of methodology and theory for the estimation of structured nonparametric regression models with Hilbertian responses. Our method and theory are focused on the additive model, while the main ideas may be adapted to other structured models. For this, the notion of Bochner integration is introduced for Banach-space-valued maps as a generalization of Lebesgue integration. Several statistical properties of Bochner integrals, relevant for our method and theory and al...
• ASYMPTOTIC DISTRIBUTION AND DETECTION THRESHOLDS FOR TWO-SAMPLE TESTS BASED ON GEOMETRIC GRAPHS AI翻译
  作者：Bhattacharya, Bhaswar B.
  作者单位：University of Pennsylvania
  摘要：In this paper, we consider the problem of testing the equality of two multivariate distributions based on geometric graphs constructed using the interpoint distances between the observations. These include the tests based on the minimum spanning tree and the K-nearest neighbor (NN) graphs, among others. These tests are asymptotically distribution-free, universally consistent and computationally efficient, making them particularly useful in modern applications. However, very little is known abo...