Journal of the Royal Statistical Society: Series B

最新文章

• Set estimation from reflected Brownian motion AI翻译
  作者：Cholaquidis, Alejandro; Fraiman, Ricardo; Lugosi, Gabor; Pateiro-Lopez, Beatriz
  作者单位：Universidad de la Republica, Uruguay; ICREA; Pompeu Fabra University; Universidade de Santiago de Compostela
  摘要：We study the problem of estimating a compact set S subset of R-d from a trajectory of a reflected Brownian motion in S with reflections on the boundary of S. We establish consistency and rates of convergence for various estimators of S and its boundary. This problem has relevant applications in ecology in estimating the home range of an animal on the basis of tracking data. There are a variety of studies on the habitat of animals that employ the notion of home range. The paper offers theoretic...
• Making the cut: improved ranking and selection for large-scale inference AI翻译
  作者：Henderson, Nicholas C.; Newton, Michael A.
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood approaches tend to favour units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized...
• Causal inference by using invariant prediction: identification and confidence intervals AI翻译
  作者：Peters, Jonas; Buhlmann, Peter; Meinshausen, Nicolai
  作者单位：Max Planck Society; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance of a prediction under a cau...
• 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...
• Distance shrinkage and Euclidean embedding via regularized kernel estimation AI翻译
  作者：Zhang, Luwan; Wahba, Grace; Yuan, Ming
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Although recovering a Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based on the so-called regularized kernel estimate. We show that such an estimate can be characterized as simply applying a constant amount of shrinkage to all observed pairwise distances. This fact allows us to establish risk bounds for the estimate, implying that the true ...
• Variable selection for support vector machines in moderately high dimensions AI翻译
  作者：Zhang, Xiang; Wu, Yichao; Wang, Lan; Li, Runze
  作者单位：North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：The support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable selection consistency, are largely unknown when the number of predictors diverges to 1. We establish a unified theory for a general class of non-c...
• Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings AI翻译
  作者：Ehm, Werner; Gneiting, Tilmann; Jordan, Alexander; Krueger, Fabian
  作者单位：Heidelberg Institute for Theoretical Studies; Helmholtz Association; Karlsruhe Institute of Technology
  摘要：In the practice of point prediction, it is desirable that forecasters receive a directive in the form of a statistical functional. For example, forecasters might be asked to report the mean or a quantile of their predictive distributions. When evaluating and comparing competing forecasts, it is then critical that the scoring function used for these purposes be consistent for the functional at hand, in the sense that the expected score is minimized when following the directive. We show that any...
• 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...
• Optimal designs for the prediction of individual parameters in hierarchical models AI翻译
  作者：Prus, Maryna; Schwabe, Rainer
  作者单位：Otto von Guericke University
  摘要：Characterizations of optimal designs are derived for the prediction of individual response curves within the framework of hierarchical linear mixed models. It is shown that the so-obtained optimal designs may differ substantially from those propagated in the literature so far and that the latter may become useless in terms of their performance.
• Tests for high dimensional generalized linear models AI翻译
  作者：Guo, Bin; Chen, Song Xi
  作者单位：Sichuan University; Peking University; Iowa State University
  摘要：We consider testing regression coefficients in high dimensional generalized linear models. By modifying the test statistic of Goeman and his colleagues for large but fixed dimensional settings, we propose a new test, based on an asymptotic analysis, that is applicable for diverging dimensions and is robust to accommodate a wide range of link functions. The power properties of the tests are evaluated asymptotically under two families of alternative hypotheses. In addition, a test in the presenc...