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作者：Zhu, Hong; Wang, Mei-Cheng
作者单位：University of Texas System; University of Texas Southwestern Medical Center; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：In many biomedical applications, interest focuses on the occurrence of two or more consecutive failure events and the relationship between event times, such as age of disease onset and residual lifetime. Bivariate survival data with interval sampling arise frequently when disease registries or surveillance systems collect data based on disease incidence occurring within a specific calendar time interval. The initial event is then retrospectively confirmed and the subsequent failure event may b...
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作者：Hanfelt, John J.; Wang, Lijia
作者单位：Emory University
摘要：When the data are sparse but not exceedingly so, we face a trade-off between bias and precision that makes the usual choice between conducting either a fully unconditional inference or a fully conditional inference unduly restrictive. We propose a method to relax the conditional inference that relies upon commonly available computer outputs. In the rectangular array asymptotic setting, the relaxed conditional maximum likelihood estimator has smaller bias than the unconditional estimator and sm...
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作者：Hu, Zonghui; Follmann, Dean A.; Wang, Naisyin
作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); University of Michigan System; University of Michigan
摘要：We introduce the effective balancing score for estimation of the mean response under a missing-at-random mechanism. Unlike conventional balancing scores, the proposed score is constructed via dimension reduction free. of model specification. Three types of such scores are introduced, distinguished by whether they carry the covariate information about the missingness, the response, or both. The effective balancing score leads to consistent estimation with little or no loss in efficiency. Compar...
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作者：Jiang, Jiancheng
作者单位：University of North Carolina; University of North Carolina Charlotte
摘要：Vector time series data are widely met in practice. In this paper we propose a multivariate functional-coefficient regression model with heteroscedasticity for modelling such data. A local linear smoother is employed to estimate the unknown coefficient matrices. Asymptotic normality of the proposed estimators is established, and bandwidth selection is considered. To deal with the co-integration commonly observed in financial markets, we propose an error-corrected multivariate functional-coeffi...
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作者：Zhang, Chong; Liu, Yufeng
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Large-margin classifiers are popular methods for classification. Among existing simultaneous multicategory large-margin classifiers, a common approach is to learn k different functions for a k-class problem with a sum-to-zero constraint. Such a formulation can be inefficient. We propose a new multicategory angle-based large-margin classification framework. The proposed angle-based classifiers consider a simplex-based prediction rule without the sum-to-zero constraint, and enjoy more efficient ...
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作者：Jia, Xiaoyu; Lee, Shing M.; Cheung, Ying Kuen
作者单位：Boehringer Ingelheim; Columbia University
摘要：This paper deals with the design of the likelihood continual reassessment method, which is an increasingly widely used model-based method for dose-finding studies. It is common to implement the method in a two-stage approach, whereby the model-based stage is activated after an initial sequence of patients has been treated. While this two-stage approach is practically appealing, it lacks a theoretical framework, and it is often unclear how the design components should be specified. This paper d...
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作者：Delaigle, A.; Hall, P.; Wishart, J. R.
作者单位：University of Melbourne; University of New South Wales Sydney
摘要：We consider nonparametric and semiparametric estimation of a conditional probability curve in the case of group testing data, where the individuals are pooled randomly into groups and only the pooled data are available. We derive a nonparametric weighted estimator that has optimality properties accounting for group sizes, and show how to extend it to multivariate settings, including the partially linear model. In the group testing context, it is natural to assume that the probability curve dep...
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作者：Vexler, A.; Tao, G.; Hutson, A. D.
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY
摘要：Posterior expectation is widely used as a Bayesian point estimator. In this note we extend it from parametric models to nonparametric models using empirical likelihood, and develop a nonparametric analogue of James Stein estimation. We use the Laplace method to establish asymptotic approximations to our proposed posterior expectations, and show by simulation that they are often more efficient than the corresponding classical nonparametric procedures, especially when the underlying data are ske...
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作者：Georgiou, S. D.; Stylianou, S.; Drosou, K.; Koukouvinos, C.
作者单位：Royal Melbourne Institute of Technology (RMIT); University of Aegean; National Technical University of Athens
摘要：This paper presents new infinite families of orthogonal designs for computer experiments. In cases where orthogonal 'designs cannot exist, we construct alternative, nearly orthogonal designs. Our designs can accommodate many factors and a large set of levels. No iterative computer search is required. To build up the desired orthogonal designs we develop and use new infinite classes of periodic Golay pairs.
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作者：Wang, D.; McMahan, C. S.; Gallagher, C. M.; Kulasekera, K. B.
作者单位：Clemson University; University of Louisville
摘要：Group testing, through the use of pooling, has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest, such as infection status. A topic of key interest in the statistical literature involves the development of regression models that relate individual-level covariates to testing responses observed from pooled specimens. In this article, we propose a general semiparametric framework that allows for the inclusion of multi-...
