2期

最新文章

• Bootstrapping the portmanteau tests in weak auto-regressive moving average models AI翻译
  作者：Zhu, Ke
  作者单位：Chinese Academy of Sciences
  摘要：The paper uses a random-weighting (RW) method to bootstrap the critical values for the Ljung-Box or Monti portmanteau tests and weighted Ljung-Box or Monti portmanteau tests in weak auto-regressive moving average models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power generalized auto-regressive conditional heteroscedasticity models. Simulation evidence indicates that t...
• Empirical likelihood confidence intervals for complex sampling designs AI翻译
  作者：Berger, Y. G.; Torres, O. De La Riva
  作者单位：University of Southampton; Instituto Nacional de Salud Publica
  摘要：We define an empirical likelihood approach which gives consistent design-based confidence intervals which can be calculated without the need of variance estimates, designeffects, resampling, joint inclusion probabilities and linearization, even when the point estimator is not linear. It can be used to construct confidence intervals for a large class of sampling designs and estimators which are solutions of estimating equations. It can be used for means, regressions coefficients, quantiles, tot...
• Joint estimation of multiple graphical models from high dimensional time series AI翻译
  作者：Qiu, Huitong; Han, Fang; Liu, Han; Caffo, Brian
  作者单位：Johns Hopkins University; Princeton University
  摘要：We consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel-based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dim...
• Detecting relevant changes in time series models AI翻译
  作者：Dette, Holger; Wied, Dominik
  作者单位：Ruhr University Bochum; Dortmund University of Technology
  摘要：Most of the literature on change point analysis by means of hypothesis testing considers hypotheses of the formH0 :theta(1=)theta(2) versus H-1 :theta(1)not equal theta(2) where theta(1) and theta(2) denote parameters of the process before and after a change point. The paper takes a different perspective and investigates the null hypotheses of no relevant changes, i. e. H-0 : parallel to theta(1) - theta(2)parallel to <=Delta, where parallel to parallel to is an appropriate norm. This formulat...
• Making a non-parametric density estimator more attractive, and more accurate, by data perturbation AI翻译
  作者：Doosti, Hassan; Hall, Peter
  作者单位：Mashhad University of Medical Sciences; University of Melbourne
  摘要：Motivated by both the shortcomings of high order density estimators, and the increasingly large data sets in many areas of modern science, we introduce new high order, non-parametric density estimators that are guaranteed to be positive and do not have highly oscillatory tails. Our approach is based on data perturbation, e.g. by tilting or data sharpening. It leads to new estimators that are more accurate than conventional kernel techniques that use positive kernels, but which nevertheless enj...
• On the coverage bound problem of empirical likelihood methods for time series AI翻译
  作者：Zhang, Xianyang; Shao, Xiaofeng
  作者单位：University of Missouri System; University of Missouri Columbia; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The upper bounds on the coverage probabilities of the confidence regions based on blockwise empirical likelihood and non-standard expansive empirical likelihood methods for time series data are investigated via studying the probability of violating the convex hull constraint. The large sample bounds are derived on the basis of the pivotal limit of the blockwise empirical log-likelihood ratio obtained under fixed b asymptotics, which has recently been shown to provide a more accurate approximat...
• Sequential selection procedures and false discovery rate control AI翻译
  作者：G'Sell, Max Grazier; Wager, Stefan; Chouldechova, Alexandra; Tibshirani, Robert
  作者单位：Carnegie Mellon University; Stanford University
  摘要：We consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block H1,...,Hk of hypotheses. A rejection rule in this setting amounts to a procedure for choosing the stopping point k. This setting is inspired by the sequential nature of many model selection problems, where choosing a stopping point or a model is equivalent to rejecting all hypotheses up to that point and none thereafter. We propose two new testing p...
• Simulation of multivariate diffusion bridges AI翻译
  作者：Bladt, Mogens; Finch, Samuel; Sorensen, Michael
  作者单位：Universidad Nacional Autonoma de Mexico; University of Copenhagen
  摘要：We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously proposed simulation method for one-dimensional bridges to the multivariate setting. First a method of simulating approximate, but often very accurate, diffusion bridges is proposed. These approximate bridge...