2期

最新文章

• A block model for node popularity in networks with community structure AI翻译
  作者：Sengupta, Srijan; Chen, Yuguo
  作者单位：Virginia Polytechnic Institute & State University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The community structure that is observed in empirical networks has been of particular interest in the statistics literature, with a strong emphasis on the study of block models. We study an important network feature called node popularity, which is closely associated with community structure. Neither the classical stochastic block model nor its degree-corrected extension can satisfactorily capture the dynamics of node popularity as observed in empirical networks. We propose a popularity-adjust...
• On structure testing for component covariance matrices of a high dimensional mixture AI翻译
  作者：Li, Weiming; Yao, Jianfeng
  作者单位：Shanghai University of Finance & Economics; University of Hong Kong
  摘要：By studying the family of p-dimensional scale mixtures, the paper shows for the first time a non-trivial example where the eigenvalue distribution of the corresponding sample covariance matrix does not converge to the celebrated Marenko-Pastur law. A different and new limit is found and characterized. The reasons for failure of the Marenko-Pastur limit in this situation are found to be a strong dependence between the p-co-ordinates of the mixture. Next, we address the problem of testing whethe...
• Matrix variate regressions and envelope models AI翻译
  作者：Ding, Shanshan; Cook, R. Dennis
  作者单位：University of Delaware; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Modern technology often generates data with complex structures in which both response and explanatory variables are matrix valued. Existing methods in the literature can tackle matrix-valued predictors but are rather limited for matrix-valued responses. We study matrix variate regressions for such data, where the response Y on each experimental unit is a random matrix and the predictor X can be either a scalar, a vector or a matrix, treated as non-stochastic in terms of the conditional distrib...
• 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...
• 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...
• 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...
• Semiparametric dynamic max-copula model for multivariate time series AI翻译
  作者：Zhao, Zifeng; Zhang, Zhengjun
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：The paper presents a novel non-linear framework for the construction of flexible multivariate dependence structure (i.e. copulas) from existing copulas based on a straightforward pairwise max-'rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared with the classical linear symmetric' mixture copula, the max-copula can be viewed as a non-linear asymmetric' framework. It is capable of modelling asymmetric dependence and joint tail behaviour while also ...
• Unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data AI翻译
  作者：Wang, Honglang; Zhong, Ping-Shou; Cui, Yuehua; Li, Yehua
  作者单位：Purdue University System; Purdue University; Purdue University in Indianapolis; Michigan State University; Iowa State University
  摘要：We consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points. We propose test procedures based on the empirical likelihood with bias-corrected estimating equations to conduct both pointwise and simultaneous inferences. The asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses, where sparse and dense ...