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作者：Zanella, Giacomo
作者单位：Bocconi University; Bocconi University
摘要：There is a lack of methodological results to design efficient Markov chain Monte Carlo (MCMC) algorithms for statistical models with discrete-valued high-dimensional parameters. Motivated by this consideration, we propose a simple framework for the design of informed MCMC proposals (i.e., Metropolis-Hastings proposal distributions that appropriately incorporate local information about the target) which is naturally applicable to discrete spaces. Using Peskun-type comparisons of Markov kernels,...
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作者：Xie, Jinhan; Lin, Yuanyuan; Yan, Xiaodong; Tang, Niansheng
作者单位：Yunnan University; Chinese University of Hong Kong; Shandong University
摘要：The populations of interest in modern studies are very often heterogeneous. The population heterogeneity, the qualitative nature of the outcome variable and the high dimensionality of the predictors pose significant challenge in statistical analysis. In this article, we introduce a category-adaptive screening procedure with high-dimensional heterogeneous data, which is to detect category-specific important covariates. The proposal is a model-free approach without any specification of a regress...
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作者：Pan, Wenliang; Wang, Xueqin; Zhang, Heping; Zhu, Hongtu; Zhu, Jin
作者单位：Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Yale University; University of Texas System; UTMD Anderson Cancer Center
摘要：Technological advances in science and engineering have led to the routine collection of large and complex data objects, where the dependence structure among those objects is often of great interest. Those complex objects (e.g., different brain subcortical structures) often reside in some Banach spaces, and hence their relationship cannot be well characterized by most of the existing measures of dependence such as correlation coefficients developed in Hilbert spaces. To overcome the limitations...
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作者：Davison, A. C.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
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作者：Yu, Shan; Wang, Guannan; Wang, Li; Liu, Chenhui; Yang, Lijian
作者单位：Iowa State University; William & Mary; Tsinghua University; Tsinghua University
摘要：In many application areas, data are collected on a count or binary response with spatial covariate information. In this article, we introduce a new class of generalized geoadditive models (GGAMs) for spatial data distributed over complex domains. Through a link function, the proposed GGAM assumes that the mean of the discrete response variable depends on additive univariate functions of explanatory variables and a bivariate function to adjust for the spatial effect. We propose a two-stage appr...
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作者：Shi, Chengchun; Lu, Wenbin; Song, Rui
作者单位：North Carolina State University
摘要：In contrast to the classical one-size-fits-all approach, precision medicine proposes the customization of individualized treatment regimes to account for patients' heterogeneity in response to treatments. Most of existing works in the literature focused on estimating optimal individualized treatment regimes. However, there has been less attention devoted to hypothesis testing regarding the existence of overall qualitative treatment effects, especially when there are a large number of prognosti...
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作者：Ke, Chenlu; Yin, Xiangrong
作者单位：University of Kentucky
摘要：We propose a novel class of independence measures for testing independence between two random vectors based on the discrepancy between the conditional and the marginal characteristic functions. The relation between our index and other similar measures is studied, which indicates that they all belong to a large framework of reproducing kernel Hilbert space. If one of the variables is categorical, our asymmetric index extends the typical ANOVA to a kernel ANOVA that can test a more general hypot...
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作者：Chen, Elynn Y.; Tsay, Ruey S.; Chen, Rong
作者单位：Princeton University; University of Chicago; Rutgers University System; Rutgers University New Brunswick
摘要：High-dimensional matrix-variate time series data are becoming widely available in many scientific fields, such as economics, biology, and meteorology. To achieve significant dimension reduction while preserving the intrinsic matrix structure and temporal dynamics in such data, Wang, Liu, and Chen proposed a matrix factor model, that is, shown to be able to provide effective analysis. In this article, we establish a general framework for incorporating domain and prior knowledge in the matrix fa...
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作者：Wang, Jingshen; He, Xuming; Xu, Gongjun
作者单位：University of Michigan System; University of Michigan
摘要：This article concerns the potential bias in statistical inference on treatment effects when a large number of covariates are present in a linear or partially linear model. While the estimation bias in an under-fitted model is well understood, we address a lesser-known bias that arises from an over-fitted model. The over-fitting bias can be eliminated through data splitting at the cost of statistical efficiency, and we show that smoothing over random data splits can be pursued to mitigate the e...
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作者：Wu, Peng; Zeng, Donglin; Wang, Yuanjia
作者单位：Columbia University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle c...
