Biometrika

最新文章

• Rejoinder: 'Nonparametric generalized fiducial inference for survival functions under censoring' AI翻译
  作者：Cui, Y.; Hannig, J.
  作者单位：University of Pennsylvania; University of North Carolina; University of North Carolina Chapel Hill
• Discussion of 'Nonparametric generalized fiducial inference for survival functions under censoring' AI翻译
  作者：Martin, Ryan
  作者单位：North Carolina State University
• Multivariate output analysis for Markov chain Monte Carlo AI翻译
  作者：Vats, Dootika; Flegal, James M.; Jones, Galin L.
  作者单位：University of Warwick; University of California System; University of California Riverside; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Markov chain Monte Carlo produces a correlated sample which may be used for estimating expectations with respect to a target distribution. A fundamental question is: when should sampling stop so that we have good estimates of the desired quantities? The key to answering this question lies in assessing the Monte Carlo error through a multivariate Markov chain central limit theorem. The multivariate nature of this Monte Carlo error has been largely ignored in the literature. We present a multiva...
• Decompositions of dependence for high-dimensional extremes AI翻译
  作者：Cooley, D.; Thibaud, E.
  作者单位：Colorado State University System; Colorado State University Fort Collins; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：We propose two decompositions that help to summarize and describe high-dimensional tail dependence within the framework of regular variation. We use a transformation to define a vector space on the positive orthant and show that transformed-linear operations applied to regularly-varying random vectors preserve regular variation. We summarize tail dependence via a matrix of pairwise tail dependence metrics that is positive semidefinite; eigendecomposition allows one to interpret tail dependence...
• Nonparametric regression with adaptive truncation via a convex hierarchical penalty AI翻译
  作者：Haris, Asad; Shojaie, Ali; Simon, Noah
  作者单位：University of Washington; University of Washington Seattle
  摘要：We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and smoothing penalties. In the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions possess diff...
• Tyler shape depth AI翻译
  作者：Paindaveine, D.; Van Bever, G.
  作者单位：Universite Libre de Bruxelles; University of Namur
  摘要：In many problems from multivariate analysis, the parameter of interest is a shape matrix: a normalized version of the corresponding scatter or dispersion matrix. In this article we propose a notion of depth for shape matrices that involves data points only through their directions from the centre of the distribution. We refer to this concept as Tyler shape depth since the resulting estimator of shape, namely the deepest shape matrix, is the median-based counterpart of the M-estimator of shape ...
• Testing for individual sphericity in heterogeneous panels AI翻译
  作者：Broda, Simon A.
  作者单位：University of Zurich
  摘要：This manuscript considers locally best invariant tests for sphericity in heterogeneous panels. A new integral representation for the characteristic function of the test statistic under the null is presented, along with an algorithm for inverting it to obtain the distribution function. A saddlepoint approximation to the null distribution addresses the need to quickly compute approximate p-values in empirical work. The approximation shows substantial improvements over the normal approximation wh...
• Estimating the error variance in a high-dimensional linear model AI翻译
  作者：Yu, Guo; Bien, Jacob
  作者单位：University of Washington; University of Washington Seattle; University of Southern California
  摘要：The lasso has been studied extensively as a tool for estimating the coefficient vector in the high-dimensional linear model; however, considerably less is known about estimating the error variance in this context. In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized likelihood objective. A key aspect of the natural lasso is that the likelihood is expressed in terms of the natural parameterization of the multi-parameter exponential family of ...
• The conditionality principle in high-dimensional regression AI翻译
  作者：Azriel, D.
  作者单位：Technion Israel Institute of Technology
  摘要：Consider a high-dimensional linear regression problem, where the number of covariates is larger than the number of observations and the interest is in estimating the conditional variance of the response variable given the covariates. A conditional and an unconditional framework are considered, where conditioning is with respect to the covariates, which are ancillary to the parameter of interest. In recent papers, a consistent estimator was developed in the unconditional framework when the marg...
• Fast exact conformalization of the lasso using piecewise linear homotopy AI翻译
  作者：Lei, J.
  作者单位：Carnegie Mellon University
  摘要：Conformal prediction is a general method that converts almost any point predictor to a prediction set. The resulting set retains the good statistical properties of the original estimator under standard assumptions, and guarantees valid average coverage even when the model is mis-specified. A main challenge in applying conformal prediction in modern applications is efficient computation, as it generally requires an exhaustive search over the entire output space. In this paper we develop an exac...