Journal of the Royal Statistical Society: Series B

最新文章

• Confidence intervals for low dimensional parameters in high dimensional linear models AI翻译
  作者：Zhang, Cun-Hui; Zhang, Stephanie S.
  作者单位：Rutgers University System; Rutgers University New Brunswick; Columbia University
  摘要：The purpose of this paper is to propose methodologies for statistical inference of low dimensional parameters with high dimensional data. We focus on constructing confidence intervals for individual coefficients and linear combinations of several of them in a linear regression model, although our ideas are applicable in a much broader context. The theoretical results that are presented provide sufficient conditions for the asymptotic normality of the proposed estimators along with a consistent...
• Multiscale change point inference AI翻译
  作者：Frick, Klaus; Munk, Axel; Sieling, Hannes
  作者单位：University of Gottingen; Max Planck Society
  摘要：We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE, for the change point problem in exponential family regression. An unknown step function is estimated by minimizing the number of change points over the acceptance region of a multiscale test at a level alpha. The probability of overestimating the true number of change points K is controlled by the asymptotic null distribution of the multiscale test statistic. Further, we derive exponential bounds for the pr...
• On semiparametric inference of geostatistical models via local Karhunen-Loeve expansion AI翻译
  作者：Chu, Tingjin; Wang, Haonan; Zhu, Jun
  作者单位：Renmin University of China; Colorado State University System; Colorado State University Fort Collins; University of Wisconsin System; University of Wisconsin Madison
  摘要：We develop a semiparametric approach to geostatistical modelling and inference. In particular, we consider a geostatistical model with additive components, where the form of the covariance function of the spatial random error is not prespecified and thus is flexible. A novel, local Karhunen-Loeve expansion is developed and a likelihood-based method is devised for estimating the model parameters and statistical inference. A simulation study demonstrates sound finite sample properties and a real...
• Semiparametric inference for data with a continuous outcome from a two-phase probability-dependent sampling scheme AI翻译
  作者：Zhou, Haibo; Xu, Wangli; Zeng, Donglin; Cai, Jianwen
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Renmin University of China
  摘要：Multiphased designs and biased sampling designs are two of the well-recognized approaches to enhance study efficiency. We propose a new and cost-effective sampling design, the two-phase probability-dependent sampling design, for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take advantage of data obtained thr...
• Conditional transformation models AI翻译
  作者：Hothorn, Torsten; Kneib, Thomas; Buehlmann, Peter
  作者单位：University of Munich; University of Zurich; University of Gottingen; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：The ultimate goal of regression analysis is to obtain information about the conditional distribution of a response given a set of explanatory variables. This goal is, however, seldom achieved because most established regression models estimate only the conditional mean as a function of the explanatory variables and assume that higher moments are not affected by the regressors. The underlying reason for such a restriction is the assumption of additivity of signal and noise. We propose to relax ...
• The deviance information criterion: 12 years on AI翻译
  作者：Spiegelhalter, David J.; Best, Nicola G.; Carlin, Bradley P.; van der Linde, Angelika
  作者单位：University of Cambridge; Imperial College London; University of Minnesota System; University of Minnesota Twin Cities
  摘要：The essentials of our paper of 2002 are briefly summarized and compared with other criteria for model comparison. After some comments on the paper's reception and influence, we consider criticisms and proposals forimprovement made by us and others.
• Optimal designs for dose finding studies with an active control AI翻译
  作者：Dette, Holger; Kiss, Christine; Benda, Norbert; Bretz, Frank
  作者单位：Ruhr University Bochum; Novartis; Shanghai University of Finance & Economics
  摘要：Dose finding studies often compare several doses of a new compound with a marketed standard treatment as an active control. In the past, however, research has focused mostly on experimental designs for placebo controlled dose finding studies. To the best of our knowledge, optimal designs for dose finding studies with an active control have not been considered so far. As the statistical analysis for an active controlled dose finding study can be formulated in terms of a mixture of two regressio...
• Two-sample test of high dimensional means under dependence AI翻译
  作者：Cai, T. Tony; Liu, Weidong; Xia, Yin
  作者单位：University of Pennsylvania; Shanghai Jiao Tong University
  摘要：The paper considers in the high dimensional setting a canonical testing problem in multivariate analysis, namely testing the equality of two mean vectors. We introduce a new test statistic that is based on a linear transformation of the data by the precision matrix which incorporates the correlations between the variables. The limiting null distribution of the test statistic and the power of the test are analysed. It is shown that the test is particularly powerful against sparse alternatives a...
• Structured functional additive regression in reproducing kernel Hilbert spaces AI翻译
  作者：Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen
  作者单位：Virginia Polytechnic Institute & State University; University of Toronto; University of Arizona
  摘要：Functional additive models provide a flexible yet simple framework for regressions involving functional predictors. The utilization of a data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting non-linear additive components has been less studied. In this work, we propose a new regularization framework for structure estimation in the context of reproducing kernel Hilbert spaces. The approach ...
• A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series AI翻译
  作者：Nason, Guy
  作者单位：University of Bristol
  摘要：Many time series are not second order stationary and it is not appropriate to analyse them by using methods designed for stationary series. The paper introduces a new test for second-order stationarity that detects kinds of departures from stationarity that are different from those based on Fourier methods. The new test is also computationally fast, designed to work with Gaussian and a wide range of non-Gaussian time series, and can locate non-stationarities in time and scale. The test is demo...