Biometrika

最新文章

• Testing parametric assumptions of trends of a nonstationary time series AI翻译
  作者：Zhang, Ting; Wu, Wei Biao
  作者单位：University of Chicago
  摘要：The paper considers testing whether the mean trend of a nonstationary time series is of certain parametric forms. A central limit theorem for the integrated squared error is derived, and a hypothesis-testing procedure is proposed. The method is illustrated in a simulation study, and is applied to assess the mean pattern of lifetime-maximum wind speeds of global tropical cyclones from 1981 to 2006. We also revisit the trend pattern in the central England temperature series.
• Bayesian isotonic density regression AI翻译
  作者：Wang, Lianming; Dunson, David B.
  作者单位：University of South Carolina System; University of South Carolina Columbia; Duke University
  摘要：Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not clear whether such priors have full support so that any true data-generating model can be accurately a...
• On balanced random imputation in surveys AI翻译
  作者：Chauvet, G.; Deville, J. -C.; Haziza, D.
  作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Montreal
  摘要：Random imputation methods are often used in practice because they tend to preserve the distribution of the variable being imputed, which is an important property when the goal is to estimate population quantiles. However, this type of imputation method introduces additional variability, the imputation variance, due to the random selection of residuals. In this paper, we propose a class of random balanced imputation methods under which the imputation variance is eliminated while the distributio...
• Sparse estimation of a covariance matrix AI翻译
  作者：Bien, Jacob; Tibshirani, Robert J.
  作者单位：Stanford University; Stanford University
  摘要：We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even when the dimension of the vectors is smaller than the sample size since the number of parameters grows quadratically in the number of variables, ...
• Elliptical graphical modelling AI翻译
  作者：Vogel, D.; Fried, R.
  作者单位：Dortmund University of Technology
  摘要：We propose elliptical graphical models based on conditional uncorrelatedness as a robust generalization of Gaussian graphical models. Letting the population distribution be elliptical instead of normal allows the fitting of data with arbitrarily heavy tails. We study the class of proportionally affine equivariant scatter estimators and show how they can be used to perform elliptical graphical modelling. This leads to a new class of partial correlation estimators and analogues of the classical ...
• A class of mixtures of dependent tail-free processes AI翻译
  作者：Jara, A.; Hanson, T. E.
  作者单位：Pontificia Universidad Catolica de Chile; University of South Carolina System; University of South Carolina Columbia
  摘要：We propose a class of dependent processes in which density shape is regressed on one or more predictors through conditional tail-free probabilities by using transformed Gaussian processes. A particular linear version of the process is developed in detail. The resulting process is flexible and easy to fit using standard algorithms for generalized linear models. The method is applied to growth curve analysis, evolving univariate random effects distributions in generalized linear mixed models, an...
• Non-Gaussian spatiotemporal modelling through scale mixing AI翻译
  作者：Fonseca, Thais C. O.; Steel, Mark F. J.
  作者单位：Universidade Federal do Rio de Janeiro; University of Warwick
  摘要：We construct non-Gaussian processes that vary continuously in space and time with nonseparable covariance functions. Starting from a general and flexible way of constructing valid nonseparable covariance functions through mixing over separable covariance functions, the resulting models are generalized by allowing for outliers as well as regions with larger variances. We induce this through scale mixing with separate positive-valued processes. Smooth mixing processes are applied to the underlyi...
• Generalized linear time series regression AI翻译
  作者：Mammen, Enno; Nielsen, Jens Perch; Fitzenberger, Bernd
  作者单位：University of Mannheim; City St Georges, University of London; University of Freiburg
  摘要：We consider a cross-section model that contains an individual component, a deterministic time trend and an unobserved latent common time series component. We show the following oracle property: the parameters of the latent time series and the parameters of the deterministic time trend can be estimated with the same asymptotic accuracy as if the parameters of the individual component were known. We consider this model in two settings: least squares fits of linear specifications of the individua...
• Joint estimation of multiple graphical models AI翻译
  作者：Guo, Jian; Levina, Elizaveta; Michailidis, George; Zhu, Ji
  作者单位：University of Michigan System; University of Michigan
  摘要：Gaussian graphical models explore dependence relationships between random variables, through the estimation of the corresponding inverse covariance matrices. In this paper we develop an estimator for such models appropriate for data from several graphical models that share the same variables and some of the dependence structure. In this setting, estimating a single graphical model would mask the underlying heterogeneity, while estimating separate models for each category does not take advantag...
• Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment AI翻译
  作者：Rosenblum, M.; van der Laan, M. J.
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of California System; University of California Berkeley
  摘要：It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial design...