Journal of the Royal Statistical Society: Series B

最新文章

• Estimation of tail risk based on extreme expectiles AI翻译
  作者：Daouia, Abdelaati; Girard, Stephane; Stupfler, Gilles
  作者单位：Universite de Toulouse; Universite Toulouse 1 Capitole; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Aix-Marseille Universite; University of Nottingham
  摘要：We use tail expectiles to estimate alternative measures to the value at risk and marginal expected shortfall, which are two instruments of risk protection of utmost importance in actuarial science and statistical finance. The concept of expectiles is a least squares analogue of quantiles. Both are M-quantiles as the minimizers of an asymmetric convex loss function, but expectiles are the only M-quantiles that are coherent risk measures. Moreover, expectiles define the only coherent risk measur...
• Report of the Editors-2017 AI翻译
  作者：Dunson, David; Fryzlewicz, Piotr
• Hybrid quantile regression estimation for time series models with conditional heteroscedasticity AI翻译
  作者：Zheng, Yao; Zhu, Qianqian; Li, Guodong; Xiao, Zhijie
  作者单位：University of Hong Kong; Shanghai University of Finance & Economics; Boston College
  摘要：Estimating conditional quantiles of financial time series is essential for risk management and many other financial applications. For time series models with conditional heteroscedasticity, although it is the generalized auto-regressive conditional heteroscedastic (GARCH) model that has the greatest popularity, quantile regression for this model usually gives rise to non-smooth non-convex optimization which may hinder its practical feasibility. The paper proposes an easy-to-implement hybrid qu...
• Testing mutual independence in high dimension via distance covariance AI翻译
  作者：Yao, Shun; Zhang, Xianyang; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Texas A&M University System; Texas A&M University College Station
  摘要：We introduce an L2-type test for testing mutual independence and banded dependence structure for high dimensional data. The test is constructed on the basis of the pairwise distance covariance and it accounts for the non-linear and non-monotone dependences among the data, which cannot be fully captured by the existing tests based on either Pearson correlation or rank correlation. Our test can be conveniently implemented in practice as the limiting null distribution of the test statistic is sho...
• Inference for empirical Wasserstein distances on finite spaces AI翻译
  作者：Sommerfeld, Max; Munk, Axel
  作者单位：University of Gottingen; Max Planck Society
  摘要：The Wasserstein distance is an attractive tool for data analysis but statistical inference is hindered by the lack of distributional limits. To overcome this obstacle, for probability measures supported on finitely many points, we derive the asymptotic distribution of empirical Wasserstein distances as the optimal value of a linear programme with random objective function. This facilitates statistical inference (e.g. confidence intervals for sample-based Wasserstein distances) in large general...
• Testing for marginal linear effects in quantile regression AI翻译
  作者：Wang, Huixia Judy; McKeague, Ian W.; Qian, Min
  作者单位：George Washington University; Columbia University
  摘要：The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive va...
• 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...