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作者：Berger, JO; De Oliveira, V; Sansó, B
作者单位：Duke University; Simon Bolivar University
摘要：Spatially varying phenomena are often modeled using Gaussian random fields, specified by their mean function and covariance function. The spatial correlation structure of these models is commonly specified to be of a certain form (e.g., spherical, power exponential, rational quadratic, or Matern) with a small number of unknown parameters. We consider objective Bayesian analysis of such spatial models, when the mean function of the Gaussian random field is specified as in a linear model. It is ...
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作者：Härdle, W; Sperlich, S; Spokoiny, V
作者单位：Humboldt University of Berlin; Universidad Carlos III de Madrid
摘要：We consider the component analysis problem for a regression model with an additive structure. The problem is to test whether some of the additive components are of polynomial structure (e.g., linear) without specifying the structure of the remaining components. A particular case is the problem of selecting the significant covariates. The method that we present is based on the wavelet transform using the Haar basis, which allows for applications under mild conditions on the design and smoothnes...
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作者：Kauermann, G; Carroll, RJ
作者单位：University of Glasgow; University of Glasgow; Texas A&M University System; Texas A&M University College Station
摘要：The sandwich estimator, also known as robust covariance matrix estimator, heteroscedasticity-consistent covariance matrix estimate, or empirical covariance matrix estimator, has achieved increasing use in the econometric literature as well as with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when the fitted parametric model fails to hold or is not even specified. Surprisingl...
