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作者：Han, Kyunghee; Mueller, Hans-Georg; Park, Byeong U.
作者单位：University of California System; University of California Davis; Seoul National University (SNU)
摘要：We propose and investigate additive density regression, a novel additive functional regression model for situations where the responses are random distributions that can be viewed as random densities and the predictors are vectors. Data in the form of samples of densities or distributions are increasingly encountered in statistical analysis and there is a need for flexible regression models that accommodate random densities as responses. Such models are of special interest for multivariate con...
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作者：Zhang, Jin-Ting; Guo, Jia; Zhou, Bu; Cheng, Ming-Yen
作者单位：National University of Singapore; Zhejiang University of Technology; Zhejiang Gongshang University; Hong Kong Baptist University
摘要：Testing the equality of two means is a fundamental inference problem. For high-dimensional data, the Hotelling's T-2-test either performs poorly or becomes inapplicable. Several modifications have been proposed to address this issue. However, most of them are based on asymptotic normality of the null distributions of their test statistics which inevitably requires strong assumptions on the covariance. We study this problem thoroughly and propose an L-2-norm based test that works under mild con...
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作者：McDonald, Daniel J.
作者单位：Indiana University System; Indiana University Bloomington
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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：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：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：Li, Han; Xu, Minxuan; Liu, Jun S.; Fan, Xiaodan
作者单位：Shenzhen University; Chinese University of Hong Kong; University of California System; University of California Los Angeles; Harvard University
摘要：In this article, we study the rank aggregation problem, which aims to find a consensus ranking by aggregating multiple ranking lists. To address the problem probabilistically, we formulate an elaborate ranking model for full and partial rankings by generalizing the Mallows model. Our model assumes that the ranked data are generated through a multistage ranking process that is explicitly governed by parameters that measure the overall quality and stability of the process. The new model is quite...
