2008

最新文章

• Multi-parameter automodels and their applications AI翻译
  作者：Hardouin, Cecile; Yao, Jian-Feng
  作者单位：heSam Universite; Universite Pantheon-Sorbonne; Universite de Rennes
  摘要：Motivated by the modelling of non-Gaussian data or positively correlated data on a lattice, extensions of Besag's automodels to exponential families with multi-dimensional parameters have been proposed recently. We provide a multiple-parameter analogue of Besag's one-dimensional result that gives the necessary form of the exponential families for the Markov random field's conditional distributions. We propose estimation of parameters by maximum pseudolikelihood and give a proof of the consiste...
• Model diagnosis for parametric regression in high-dimensional spaces AI翻译
  作者：Stute, W.; Xu, W. L.; Zhu, L. X.
  作者单位：Justus Liebig University Giessen; Renmin University of China; Hong Kong Baptist University
  摘要：We study tools for checking the validity of a parametric regression model. When the dimension of the regressors is large, many of the existing tests face the curse of dimensionality or require some ordering of the data. Our tests are based on the residual empirical process marked by proper functions of the regressors. They are able to detect local alternatives converging to the null at parametric rates. Parametric and nonparametric alternatives are considered. In the latter case, through a pro...
• Small area estimation when auxiliary information is measured with error AI翻译
  作者：Ybarra, Lynn M. R.; Lohr, Sharon L.
  作者单位：Arizona State University; Arizona State University-Tempe
  摘要：Small area estimation methods typically combine direct estimates from a survey with predictions from a model in order to obtain estimates of population quantities with reduced mean squared error. When the auxiliary information used in the model is measured with error, using a small area estimator such as the Fay-Herriot estimator while ignoring measurement error may be worse than simply using the direct estimator. We propose a new small area estimator that accounts for sampling variability in ...
• Population intervention models in causal inference AI翻译
  作者：Hubbard, Alan E.; Van der Laan, Mark J.
  作者单位：University of California System; University of California Berkeley
  摘要：We propose a new causal parameter, which is a natural extension of existing approaches to causal inference such as marginal structural models. Modelling approaches are proposed for the difference between a treatment-specific counterfactual population distribution and the actual population distribution of an outcome in the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population and therefore we refer to these models as populatio...
• Joint modelling of paired sparse functional data using principal components AI翻译
  作者：Zhou, Lan; Huang, Jianhua Z.; Carroll, Raymond J.
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：We propose a modelling framework to study the relationship between two paired longitudinally observed variables. The data for each variable are viewed as smooth curves measured at discrete time-points plus random errors. While the curves for each variable are summarized using a few important principal components, the association of the two longitudinal variables is modelled through the association of the principal component scores. We use penalized splines to model the mean curves and the prin...
• Predicting future responses based on possibly mis-specified working models AI翻译
  作者：Cai, Tianxi; Tian, Lu; Solomon, Scott D.; Wei, L. J.
  作者单位：Harvard University; Northwestern University; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital
  摘要：Under a general regression setting, we propose an optimal unconditional prediction procedure for future responses. The resulting prediction intervals or regions have a desirable average coverage level over a set of covariate vectors of interest. When the working model is not correctly specified, the traditional conditional prediction method is generally invalid. On the other hand, one can empirically calibrate the above unconditional procedure and also obtain its crossvalidated counterpart. Va...
• Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families AI翻译
  作者：Diciccio, Thomas J.; Young, Alastair
  作者单位：Cornell University; Imperial College London
  摘要：Higher-order inference about a scalar parameter in the presence of nuisance parameters can be achieved by bootstrapping, in circumstances where the parameter of interest is a component of the canonical parameter in a full exponential family. The optimal test, which is approximated, is a conditional one based on conditioning on the sufficient statistic for the nuisance parameter. A bootstrap procedure that ignores the conditioning is shown to have desirable conditional properties in providing t...
• Semiparametric model-based inference in the presence of missing responses AI翻译
  作者：Wang, Qihua; Dai, Pengjie
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
  摘要：We consider a semiparametric model that parameterizes the conditional density of the response, given covariates, but allows the marginal distribution of the covariates to be completely arbitrary. Responses may be missing. A likelihood-based imputation estimator and a semi-empirical-likelihood-based estimator for the parameter vector describing the conditional density are defined and proved to be asymptotically normal. Semi-empirical loglikelihood functions for the parameter vector and the resp...
• Analysis of least absolute deviation AI翻译
  作者：Chen, Kani; Ying, Zhiliang; Zhang, Hong; Zhao, Lincheng
  作者单位：Hong Kong University of Science & Technology; Columbia University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：We develop a unified L-1-based analysis-of-variance-type method for testing linear hypotheses. Like the classical L-2-based analysis of variance, the method is coordinate-free in the sense that it is invariant under any linear transformation of the covariates or regression parameters. Moreover, it allows singular design matrices and heterogeneous error terms. A simple approximation using stochastic perturbation is proposed to obtain cut-off values for the resulting test statistics. Both test s...
• Distortion of effects caused by indirect confounding AI翻译
  作者：Wermuth, Nanny; Cox, D. R.
  作者单位：Chalmers University of Technology; University of Oxford
  摘要：Undetected confounding may severely distort the effect of an explanatory variable on a response variable, as defined by a stepwise data-generating process. The best known type of distortion, which we call direct confounding, arises from an unobserved explanatory variable common to a response and its main explanatory variable of interest. It is relevant mainly for observational studies, since it is avoided by successful randomization. By contrast, indirect confounding, which we identify in this...