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作者：Diaz, Ivan; Williams, Nicholas; Hoffman, Katherine L.; Schenck, Edward J.
作者单位：Cornell University; Weill Cornell Medicine; Cornell University; Weill Cornell Medicine
摘要：Most causal inference methods consider counterfactual variables under interventions that set the exposure to a fixed value. With continuous or multi-valued treatments or exposures, such counterfactuals may be of little practical interest because no feasible intervention can be implemented that would bring them about. Longitudinal modified treatment policies (LMTPs) are a recently developed nonparametric alternative that yield effects of immediate practical relevance with an interpretation in t...
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作者：Wang, Jiangzhou; Zhang, Jingfei; Liu, Binghui; Zhu, Ji; Guo, Jianhua
作者单位：Northeast Normal University - China; Northeast Normal University - China; Southern University of Science & Technology; University of Miami; University of Michigan System; University of Michigan
摘要：The stochastic block model is one of the most studied network models for community detection, and fitting its likelihood function on large-scale networks is known to be challenging. One prominent work that overcomes this computational challenge is the fast pseudo-likelihood approach proposed by Amini et al. for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and may not be well suited for small and medium scale networks. In ...
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作者：Park, Chan; Kang, Hyunseung
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The article presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known co...
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作者：Crucinio, Francesca R.; Doucet, Arnaud; Johansen, Adam M.
作者单位：University of Warwick; University of Oxford; Alan Turing Institute
摘要：Fredholm integral equations of the first kind are the prototypical example of ill-posed linear inverse problems. They model, among other things, reconstruction of distorted noisy observations and indirect density estimation and also appear in instrumental variable regression. However, their numerical solution remains a challenging problem. Many techniques currently available require a preliminary discretization of the domain of the solution and make strong assumptions about its regularity. For...
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作者：Ni, Yang
作者单位：Texas A&M University System; Texas A&M University College Station
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作者：Deshpande, Yash; Javanmard, Adel; Mehrabi, Mohammad
作者单位：Massachusetts Institute of Technology (MIT); University of Southern California
摘要：Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference, however, adaptive data collection induces memory and correlation in the samples, and poses significant challenge. We consider the high-dimensional linear regression, where the samples are collected adaptively, and the sample size n can be smaller than p, the number of covariates. In this setting, there are two distinct sources of bias: the first due to...
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作者：Chen, Elynn Y.; Fan, Jianqing
作者单位：University of California System; University of California Berkeley; Princeton University
摘要：This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p x q) is comparable to or greater than the number of observations (T). We propose an estimation method called alpha-PCA that preserves the matrix structure and aggregates mean and contemporary covariance through a hyper-parameter alpha. We develop an inferential theory, establishing consistency, the rate of convergence, a...
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作者：Kong, Xin-Bing; Lin, Jin-Guan; Liu, Cheng; Liu, Guang-Ying
作者单位：Nanjing Audit University; Wuhan University
摘要：In this article, we study the discrepancy between the global principal component analysis (GPCA) and local principal component analysis (LPCA) in recovering the common components of a large-panel high-frequency data. We measure the discrepancy by the total sum of squared differences between common components reconstructed from GPCA and LPCA. The asymptotic distribution of the discrepancy measure is provided when the factor space is time invariant as the dimension p and sample size n tend to in...
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作者：Paparoditis, Efstathios; Shang, Han Lin
作者单位：University of Cyprus; Macquarie University
摘要：A bootstrap procedure for constructing prediction bands for a stationary functional time series is proposed. The procedure exploits a general vector autoregressive representation of the time-reversed series of Fourier coefficients appearing in the Karhunen-Loeve representation of the functional process. It generates backward-in-time functional replicates that adequately mimic the dependence structure of the underlying process in a model-free way and have the same conditionally fixed curves at ...
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作者：Zhu, Yunzhang; Shen, Xiaotong; Jiang, Hui; Wong, Wing Hung
作者单位：University System of Ohio; Ohio State University; University of Minnesota System; University of Minnesota Twin Cities; University of Michigan System; University of Michigan; Stanford University
摘要：In multilabel classification, strong label dependence is present for exploiting, particularly for word-to-word dependence defined by semantic labels. In such a situation, we develop a collaborative-learning framework to predict class labels based on label-predictor pairs and label-only data. For example, in image categorization and recognition, language expressions describe the content of an image together with a large number of words and phrases without associated images. This article propose...
