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作者：Yan, Xiaohan; Bien, Jacob
摘要：It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such rare features has received little attention despite its prevalence in diverse areas, ranging from natural language processing (e.g., rare words) to biology (e.g., rare species). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can...
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作者：Ji, Zhicheng; Ji, Hongkai
作者单位：Duke University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
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作者：Li, Sijia; Li, Xiudi; Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
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作者：Choe, Youngjun
作者单位：University of Washington; University of Washington Seattle
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作者：Dryden, Ian L.; Kume, Alfred; Paine, Phillip J.; Wood, Andrew T. A.
作者单位：University of Nottingham; University of Kent; University of Sheffield; Australian National University
摘要：In this article, we propose a regression model for size-and-shape response data. So far as we are aware, few such models have been explored in the literature to date. We assume a Gaussian model for labeled landmarks; these landmarks are used to represent the random objects under study. The regression structure, assumed in this article to be linear in the ambient space, enters through the landmark means. Two approaches to parameter estimation are considered. The first approach is based directly...
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作者：Chu, Chi Wing; Sit, Tony; Xu, Gongjun
作者单位：Columbia University; Chinese University of Hong Kong; University of Michigan System; University of Michigan
摘要：We propose a class of power-transformed linear quantile regression models for time-to-event observations subject to censoring. By introducing a process of power transformation with different transformation parameters at individual quantile levels, our framework relaxes the assumption of logarithmic transformation on survival times and provides dynamic estimation of various quantile levels. With such formulation, our proposal no longer requires the potentially restrictive global linearity assum...
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作者：Tang, Francesca; Feng, Yang; Chiheb, Hamza; Fan, Jianqing
作者单位：Princeton University; New York University
摘要：With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the United States and the rest of the world are still suffering from the effects of the virus, the importance of assigning growth membership to counties and understanding the determinants of the growth is increasingly evident. For the two communities (faster versus slower growth traject...
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作者：Chen, Haoyu; Lu, Wenbin; Song, Rui
作者单位：North Carolina State University
摘要：Online decision making aims to learn the optimal decision rule by making personalized decisions and updating the decision rule recursively. It has become easier than before with the help of big data, but new challenges also come along. Since the decision rule should be updated once per step, an offline update which uses all the historical data is inefficient in computation and storage. To this end, we propose a completely online algorithm that can make decisions and update the decision rule on...
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作者：Yan, Bowei; Sarkar, Purnamrita
作者单位：University of Texas System; University of Texas Austin
摘要：In this article, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial view of the cluster structure. One needs to jointly infer the full cluster structure by considering both. In statistics, an emerging body of work has been focused on combining information from both the edges in the network and the node covariates to infer community memberships. However, so far the theoretical guarantees ...
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作者：[Anonymous]
