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作者：Heng, Pei; He, Shiyuan; Sun, Yi; Guo, Jianhua
作者单位：Northeast Normal University - China; Beijing Technology & Business University
摘要：Collapsibility provides a principled approach to dimension reduction in contingency tables and graphical models. Madigan & Mosurski (1990) pioneered the study of minimal collapsible sets in decomposable models, but existing algorithms for general graphs remain computationally demanding. We show that a model is collapsible on to a target set precisely when that set contains at least one minimal separator between its nonadjacent vertices. This insight motivates the close minimal separator absorp...
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作者：Li, Yunchen; Wang, Guanghui; Xu, Shuntuo; Yu, Zhou
作者单位：East China Normal University; Nankai University; Nankai University
摘要：We propose CUSUM-Net, a nonparametric method for changepoint detection based on integral probability metrics and deep neural networks. Our approach learns a critic function by maximizing an aggregate CUSUM objective over candidate changepoints, thereby linking changepoint detection to optimization of two-sample integral probability metrics. The learned critic induces a one-dimensional representation on which changepoints are localized by a classical CUSUM scan. Unlike parametric procedures, CU...
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作者：Sengupta, Saikat; Khamaru, Koulik; Ghosh, Suvrojit; Dasgupta, Tirthankar
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Rutgers University System; Rutgers University New Brunswick
摘要：We study the problem of estimating the average treatment effect under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, the setting we consider is one in which the probability of assigning treatment to each experimental unit may depend on prior assignments and observed outcomes. Within the potential outcomes framework (), we propose and analyse two natural estimators for the average treatment effect: the inverse propensity weighted e...
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作者：Zhang, Bo; Zhang, Zhixiang; Pan, Guangming
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Macau; Nanyang Technological University
摘要：We consider the problem of estimating the number of significant components in high-dimensional principal component analysis. We propose a new penalized approach using the explained variance ratio and the rigidity of the nonspiked sample eigenvalues of sample covariance matrices of $ p $ variables. Compared with methods in the existing literature, the consistency of the proposed estimator holds, not only for independent data, but also for some times series data when the dimension $ p $ and the ...
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作者：Bodik, Juraj; Chavez-Demoulin, Valerie
作者单位：University of Lausanne
摘要：We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one target variable (local causal...
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作者：Murphy, Caitrin; Laber, Eric; Merwin, Rhonda; Reich, Brian; Koerner, Jake
作者单位：Duke University; Duke University; North Carolina State University
摘要：Functional principal component analysis is a key tool in the study of functional data, driving both exploratory analyses and feature construction for use in formal modelling and testing procedures. However, existing methods do not apply when functional observations are censored; for example, when the measurement instrument only supports recordings within a prespecified interval, thereby truncating values outside this range to the nearest boundary. A na & iuml;ve application of existing methods...
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作者：McClean, A.; Li, Y.; Bae, S.; McAdams DeMarco, M.; Diaz, I; Wu, W.
作者单位：New York University; New York University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, each targeting adifferent treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption: every subject has a nonzero probability of receiving each treatment. This assumption is often implausible, especially when treatment can take many values. Causal parameters based on dynamic ...
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作者：Bing, Xin; Kong, Dehan; Li, Bingqing
作者单位：University of Toronto; University of Toronto
摘要：Gaussian mixture models are fundamental statistical tools for modelling heterogeneous data. Due to the nonconcavity of the likelihood function, the expectation-maximization (EM) algorithm is widely used for parameter estimation of each Gaussian component. Existing analyses of the EM algorithm's convergence to the true parameter focus on either the two-component case or multi-component settings with known mixing probabilities and isotropic covariance matrices. In this work, we study the converg...
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作者：Farina, Rebecca; Tchetgen Tchetgen, Eric; Kuchibhotla, Arun Kumar
作者单位：Carnegie Mellon University; University of Pennsylvania; Carnegie Mellon University
摘要：Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right censoring with guaranteed coverage. Inspired by modern conformal inference, our approach avoids the need for a well-specified parametric or semiparametric survival model. Unlike existing conformal methods for survival data, which assume Type-I censoring with fully observed censoring times, we consider the more common right-censoring setting in which only the censoring time or the event ti...
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作者：Banerjee, Bilol; Bhattacharya, Bhaswar B.; Ghosh, Anil K.
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; University of Pennsylvania
摘要：In this paper we introduce a new measure of conditional dependence between two random vectors $ {\boldsymbol{X}} $ and $ {\boldsymbol{Y}} $, given another random vector $ \boldsymbol{Z} $ using the ball divergence. Our measure characterizes conditional independence and does not require any moment assumptions. We propose an estimator of the measure using a kernel-averaging technique and derive its asymptotic distribution. Using this estimator, we construct a test for conditional independence ba...
