2018

最新文章

• Estimated Wold representation and spectral-density-driven bootstrap for time series AI翻译
  作者：Krampe, Jonas; Kreiss, Jens-Peter; Paparoditis, Efstathios
  作者单位：Braunschweig University of Technology; University of Cyprus
  摘要：The second-order dependence structure of purely non-deterministic stationary processes is described by the coefficients of the famous Wold representation. These coefficients can be obtained by factorizing the spectral density of the process. This relationship together with some spectral density estimator is used to obtain consistent estimators of these coefficients. A spectral-density-driven bootstrap for time series is then developed which uses the entire sequence of estimated moving average ...
• From multiple Gaussian sequences to functional data and beyond: a Stein estimation approach AI翻译
  作者：Koudstaal, Mark; Yao, Fang
  作者单位：University of Toronto; Peking University
  摘要：We expand the notion of Gaussian sequence models to n experiments and propose a Stein estimation strategy which relies on pooling information across experiments. An oracle inequality is established to assess conditional risks given the underlying effects, based on which we can quantify the size of relative error and obtain a tuning-free recovery strategy that is easy to compute, produces model parsimony and extends to unknown variance. We show that the simultaneous recovery is adaptive to an o...
• On mitigating the analytical limitations of finely stratified experiments AI翻译
  作者：Fogarty, Colin B.
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：Although attractive from a theoretical perspective, finely stratified experiments such as paired designs suffer from certain analytical limitations that are not present in block-randomized experiments with multiple treated and control individuals in each block. In short, when using a weighted difference in means to estimate the sample average treatment effect, the traditional variance estimator in a paired experiment is conservative unless the pairwise average treatment effects are constant ac...
• Auxiliary gradient-based sampling algorithms AI翻译
  作者：Titsias, Michalis K.; Papaspiliopoulos, Omiros
  作者单位：Athens University of Economics & Business; ICREA; Pompeu Fabra University
  摘要：We introduce a new family of Markov chain Monte Carlo samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density. Our approach permits the marginalization over the auxiliary variables, yielding marginal samplers, or the augmentation of the auxiliary variables, yielding auxiliary samplers. The well-known Metropolis-adjusted Langevin algorithm MALA and preconditioned Crank-Nicolson-Langevin algorithm pCNL are shown to be special cases. We prove that mar...
• Goodness-of-fit tests for high dimensional linear models AI翻译
  作者：Shah, Rajen D.; Buhlmann, Peter
  作者单位：University of Cambridge
  摘要：We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linear models. We advocate applying regression methods to the scaled residuals following either an ordinary least squares or lasso fit to the data, and using some proxy for prediction error as the final test statistic. We call this family residual prediction tests. We show that simulation can be used to obtain the critical values for such tests in the low dimensional setting and demonstrate using bot...
• Bayesian inference for Gaussian graphical models beyond decomposable graphs AI翻译
  作者：Khare, Kshitij; Rajaratnam, Bala; Saha, Abhishek
  作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis; University of California System; University of California Davis
  摘要：Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that, when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general non-decomposable setting. Penalized likelihood inference in contrast has made tremendous gains in the past few years in terms of scalability and tractability. Bayesian inference, however, has not had the same level of success, though a scalable Bayesian approach has ...
• False discovery proportion estimation by permutations: confidence for significance analysis of microarrays AI翻译
  作者：Hemerik, Jesse; Goeman, Jelle J.
  作者单位：Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
  摘要：Significance analysis of microarrays (SAM) is a highly popular permutation-based multiple-testing method that estimates the false discovery proportion (FDP): the fraction of false positive results among all rejected hypotheses. Perhaps surprisingly, until now this method had no known properties. This paper extends SAM by providing 1- upper confidence bounds for the FDP, so that exact confidence statements can be made. As a special case, an estimate of the FDP is obtained that underestimates th...
• Random-walk models of network formation and sequential Monte Carlo methods for graphs AI翻译
  作者：Bloem-Reddy, Benjamin; Orbanz, Peter
  作者单位：University of Oxford; Columbia University
  摘要：We introduce a class of generative network models that insert edges by connecting the starting and terminal vertices of a random walk on the network graph. Within the taxonomy of statistical network models, this class is distinguished by permitting the location of a new edge to depend explicitly on the structure of the graph, but being nonetheless statistically and computationally tractable. In the limit of infinite walk length, the model converges to an extension of the preferential attachmen...
• The correlated pseudomarginal method AI翻译
  作者：Deligiannidis, George; Doucet, Arnaud; Pitt, Michael K.
  作者单位：University of Oxford; University of London; King's College London
  摘要：The pseudomarginal algorithm is a Metropolis-Hastings-type scheme which samples asymptotically from a target probability density when we can only estimate unbiasedly an unnormalized version of it. In a Bayesian context, it is a state of the art posterior simulation technique when the likelihood function is intractable but can be estimated unbiasedly by using Monte Carlo samples. However, for the performance of this scheme not to degrade as the number T of data points increases, it is typically...
• An imputation-regularized optimization algorithm for high dimensional missing data problems and beyond AI翻译
  作者：Liang, Faming; Jia, Bochao; Xue, Jingnan; Li, Qizhai; Luo, Ye
  作者单位：Purdue University System; Purdue University; State University System of Florida; University of Florida; Chinese Academy of Sciences
  摘要：Missing data are frequently encountered in high dimensional problems, but they are usually difficult to deal with by using standard algorithms, such as the expectation-maximization algorithm and its variants. To tackle this difficulty, some problem-specific algorithms have been developed in the literature, but there still lacks a general algorithm. This work is to fill the gap: we propose a general algorithm for high dimensional missing data problems. The algorithm works by iterating between a...