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作者：Leng, Chenlei; Li, Bo
作者单位：National University of Singapore; Tsinghua University
摘要：We propose a simple forward adaptive banding method for estimating large covariance matrices using the modified Cholesky decomposition. This approach requires the fitting of a prespecified set of models due to the adaptive banding structure and can be efficiently implemented. Aside from its computational attractiveness, we propose a novel Bayes information criterion that gives consistent model selection for estimating high dimensional covariance matrices. The method compares favourably to its ...
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作者：Papathomas, M.; Dellaportas, P.; Vasdekis, V. G. S.
作者单位：Imperial College London; Athens University of Economics & Business
摘要：We propose a novel methodology to construct proposal densities in reversible jump algorithms that obtain samples from parameter subspaces of competing generalized linear models with differing dimensions. The derived proposal densities are not restricted to moves between nested models and are applicable even to models that share no common parameters. We illustrate our methodology on competing logistic regression and log-linear graphical models, demonstrating how our suggested proposal densities...
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作者：Neuhaus, John M.; McCulloch, Charles E.
作者单位：University of California System; University of California San Francisco
摘要：In standard regression analyses of clustered data, one typically assumes that the expected value of the response is independent of cluster size. However, this is often false. For example, in studies of surgical interventions, investigators have frequently found surgery volume and outcomes to be related to the skill level of the surgeons. This paper examines the effect of ignoring response-dependent, informative, cluster sizes on standard analytical methods such as mixed-effects models and cond...
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作者：Feng, Xingdong; He, Xuming; Hu, Jianhua
作者单位：Shanghai University of Finance & Economics; University of Michigan System; University of Michigan; University of Texas System; UTMD Anderson Cancer Center
摘要：The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on...
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作者：Drton, Mathias; Williams, Benjamin
作者单位：University of Chicago; University of Chicago
摘要：When testing geometrically irregular parametric hypotheses, the bootstrap is an intuitively appealing method to circumvent difficult distribution theory. It has been shown, however, that the usual bootstrap is inconsistent in estimating the asymptotic distributions involved in such problems. This paper is concerned with the asymptotic size of likelihood ratio tests when critical values are computed using the inconsistent bootstrap. We clarify how the asymptotic size of such a test can be obtai...
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作者：De Luna, Xavier; Waernbaum, Ingeborg; Richardson, Thomas S.
作者单位：Umea University; University of Washington; University of Washington Seattle
摘要：Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance...
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作者：Chaudhuri, Sanjay; Ghosh, Malay
作者单位：National University of Singapore; State University System of Florida; University of Florida
摘要：Current methodologies in small area estimation are mostly either parametric or heavily dependent on the assumed linearity of the estimators of the small area means. We discuss an alternative empirical likelihood-based Bayesian approach, which neither requires a parametric likelihood nor assumes linearity of the estimators, and can handle both discrete and continuous data in a unified manner. Empirical likelihoods for both area- and unit-level models are introduced. We discuss the suitability o...
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作者：Samia, Noelle I.; Chan, Kung-Sik
作者单位：Northwestern University; University of Iowa
摘要：There is hardly any literature on modelling nonlinear dynamic relations involving nonnormal time series data. This is a serious lacuna because nonnormal data are far more abundant than normal ones, for example, time series of counts and positive time series. While there are various forms of nonlinearities, the class of piecewise-linear models is particularly appealing for its relative ease of tractability and interpretation. We propose to study the generalized threshold model which specifies t...
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作者：Zhu, Hongtu; Ibrahim, Joseph G.; Tang, Niansheng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Yunnan University
摘要：In this paper we develop a general framework of Bayesian influence analysis for assessing various perturbation schemes to the data, the prior and the sampling distribution for a class of statistical models. We introduce a perturbation model to characterize these various perturbation schemes. We develop a geometric framework, called the Bayesian perturbation manifold, and use its associated geometric quantities including the metric tensor and geodesic to characterize the intrinsic structure of ...
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作者：Kuruwita, C. N.; Kulasekera, K. B.; Gallagher, C. M.
作者单位：Clemson University
摘要：We propose a new estimation method for generalized varying coefficient models where the link function is specified up to some smoothness conditions. Consistency and asymptotic normality of the estimated varying coefficient functions are established. Simulation results and a real data application demonstrate the usefulness of the new method.
