Biometrika

最新文章

• A sum characterization of hidden regular variation with likelihood inference via expectation-maximization AI翻译
  作者：Weller, Grant B.; Cooley, Daniel
  作者单位：Carnegie Mellon University; Colorado State University System; Colorado State University Fort Collins
  摘要：A fundamental deficiency of classical multivariate extreme value theory is the inability to distinguish between asymptotic independence and exact independence. In this work, we examine multivariate threshold modelling in the framework of regular variation on cones. Tail dependence is described by a limiting measure, which in some cases is degenerate on joint tail regions despite strong subasymptotic dependence in such regions. Hidden regular variation, a higher-order tail decay on these region...
• Better subset regression AI翻译
  作者：Xiong, Shifeng
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
  摘要：This paper studies the relationship between model fitting and screening performance to find efficient screening methods for high-dimensional linear regression models. Under a sparsity assumption we show in a general asymptotic setting that a subset that includes the true submodel always yields a smaller residual sum of squares than those that do not. To seek such a subset, we consider the optimization problem associated with best subset regression. An em algorithm, known as orthogonalizing sub...
• Miscellanea Nearly orthogonal arrays mappable into fully orthogonal arrays AI翻译
  作者：Mukerjee, Rahul; Sun, Fasheng; Tang, Boxin
  作者单位：Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; Northeast Normal University - China; Simon Fraser University
  摘要：We develop a method for construction of arrays which are nearly orthogonal, in the sense that each column is orthogonal to a large proportion of the other columns, and which are convertible to fully orthogonal arrays via a mapping of the symbols in each column to a possibly smaller set of symbols. These arrays can be useful in computer experiments as designs which accommodate a large number of factors and enjoy attractive space-filling properties. Our construction allows both the mappable near...
• Estimation from cross-sectional samples under bias and dependence AI翻译
  作者：Mandel, Micha; Rinott, Yosef
  作者单位：Hebrew University of Jerusalem
  摘要：A population can be entered at a known sequence of discrete times; it is sampled cross-sectionally, and the sojourn times of individuals in the sample are observed. It is well known that cross-sectioning leads to length-bias, but less well known and often ignored that it may also result in dependence among the observations. We show that observed sojourn times are independent only under a multinomial entrance process. We study asymptotic properties of parametric and nonparametric estimators of ...
• Inference on multiple correlation coefficients with moderately high dimensional data AI翻译
  作者：Zheng, Shurong; Jiang, Dandan; Bai, Zhidong; He, Xuming
  作者单位：Northeast Normal University - China; Jilin University; University of Michigan System; University of Michigan
  摘要：When the multiple correlation coefficient is used to measure how strongly a given variable can be linearly associated with a set of covariates, it suffers from an upward bias that cannot be ignored in the presence of a moderately high dimensional covariate. Under an independent component model, we derive an asymptotic approximation to the distribution of the squared multiple correlation coefficient that depends on a simple correction factor. We show that this approximation enables us to constr...
• A combined estimating function approach for fitting stationary point process models AI翻译
  作者：Deng, C.; Waagepetersen, R. P.; Guan, Y.
  作者单位：Yale University; Aalborg University; University of Miami
  摘要：A composite likelihood technique based on pairwise contributions provides a computationally simple but potentially inefficient approach for fitting spatial point process models. We propose a new estimation procedure that improves the efficiency. Our approach combines estimating functions derived from pairwise composite likelihood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial poin...
• Sequential combination of weighted and nonparametric bagging for classification AI翻译
  作者：Soleymani, M.; Lee, S. M. S.
  作者单位：University of Hong Kong
  摘要：We propose a simple sequential procedure for bagged classification, which modifies nonparametric bagging by randomizing class labels of resampled data points. The random labelling feature of the procedure also enables us to undertake unsupervised classification with the benefit of supervised learning. Theoretical properties are given for the nearest neighbour classifier in the case of supervised learning and a hard-thresholding indicator in the case of unsupervised learning, showing that seque...
• Efficient inference for spatial extreme value processes associated to log-Gaussian random functions AI翻译
  作者：Wadsworth, Jennifer L.; Tawn, Jonathan A.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Lancaster University
  摘要：Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, inference for max-stable processes has been restricted to the use of pairwise composite likelihoods, due to intractability of higher-dimensional distributions. In this work we consider random fields that are in the domain of attraction of a widely ...
• The asymptotic inadmissibility of the spatial sign covariance matrix for elliptically symmetric distributions AI翻译
  作者：Magyar, Andrew F.; Tyler, David E.
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：The asymptotic efficiency of the spatial sign covariance matrix relative to affine equivariant estimators of scatter is studied. In particular, the spatial sign covariance matrix is shown to be asymptotically inadmissible, i.e., the asymptotic covariance matrix of the consistency-corrected spatial sign covariance matrix is uniformly larger than that of its affine equivariant counterpart, namely Tyler's scatter matrix. Although the spatial sign covariance matrix has often been recommended when ...
• Retrospective-prospective symmetry in the likelihood and Bayesian analysis of case-control studies AI翻译
  作者：Byrne, Simon P. J.; Dawid, A. Philip
  作者单位：University of London; University College London; University of Cambridge
  摘要：Prentice & Pyke (1979) established that the maximum likelihood estimate of an odds ratio in a case-control study is the same as would be found by fitting a logistic regression; in other words, for this specific target the incorrect prospective model is inferentially equivalent to the correct retrospective model. Similar results have been obtained for other models, and conditions have also been identified under which the corresponding Bayesian property holds, namely that the posterior distribut...